FICO

Fraud in the Age of AI: What Global Financial Institutions Are Telling Us

Adam Davies — Fri, 10 Apr 2026 22:38:07 +0000

Fraud in the Age of AI: What Global Financial Institutions Are Telling Us

The fraud landscape isn't just evolving — it's transforming. And the data proves it.

Tue, 04/14/2026 - 15:16

JenniferPiccinino@fico.com

by Adam Davies

Vice President, Product Management

Tue, 04/14/2026 - 12:00

Finextra Research, in association with FICO, surveyed 202 fraud management professionals across NORAM, EMEA, LATAM, and APAC to understand how fraud is changing, how organizations are responding, and where the critical gaps in enterprise fraud strategy remain. The findings are a global wake-up call.

Fraud is no longer a contained operational risk. It has become a sustained, enterprise-level financial threat — one that is being turbocharged by artificial intelligence. From deepfake-enabled account takeovers, sophisticated AI-based bot attacks, to AI-generated social engineering at scale, the threat curve has gotten dramatically steeper. And the industry's response, while showing real momentum, has an opportunity to accelerate the impact and scope of their prevention efforts.

Key Takeaways

AI is supercharging fraud at a large scale.
Fraud attempts have surged dramatically, with 26% of organizations reporting increases of over 51% in the past two years. AI has lowered the barrier to entry for fraudsters, driving synthetic identity creation, deepfake-enabled account takeovers, systemic and automated AI-driven bot attacks, and large-scale social engineering attacks aimed at scamming potential victims, that were once only possible for sophisticated criminal networks.

Most organizations recognize the problem but haven't solved it yet.
While 99% of organizations acknowledge the need for a unified, enterprise-wide fraud strategy, only 28% have fully deployed AI/ML-driven fraud detection at scale. The biggest obstacle isn't technology, it's integration. 47% of the respondents citing difficulty embedding AI effectively into their existing fraud control frameworks.

False positives are a significant hidden cost.
One in three organizations reports high or very high false positive rates, which erode customer trust and drive churn, particularly in high-growth digital markets like APAC (50% high/very high). EMEA's relatively strong performance suggests that regulatory maturity and optimized systems can meaningfully reduce this problem.

Orchestration is the missing link.
Fragmented, siloed fraud tools leave gaps even when individual models are sophisticated. The organizations best positioned to win are those building unified decisioning frameworks that orchestrate internal models, third-party vendor intelligence, and consortium data in real time across channels, products, and customer journeys.

The next frontier is agentic AI — and investment is spreading across multiple priorities.
Nearly all organizations (99%) plan to leverage agentic AI within 24 months. Investment is broadly distributed across identity verification, AI/ML detection, and real-time decisioning, signaling that institutions understand there's no single silver bullet: resilience requires advancing on multiple fronts simultaneously.

The Rising Cost of Fraud: Volume, Losses, and AI as an Accelerant

Over a quarter of organizations (26%) have seen fraud attempt increases of more than 51% over the past 24 months, and 22% report fraud-related financial losses increasing by the same margin. Higher attempt volumes are translating directly into monetary impact, a signal that organizations are not yet absorbing or deflecting these attacks effectively enough.

What's driving this surge? Artificial intelligence in the hands of fraudsters. AI has become a great equalizer, enabling previously unsophisticated bad actors to execute attacks that were once the exclusive domain of advanced criminal organizations.

Application fraud — including synthetic identity creation and account takeover — is among the fastest-growing threats. Synthetic identity fraud is seeing its greatest increase in the 61–80% growth range, with NORAM and APAC leading high-end growth at 14% reporting increases above 81%. This type of application fraud is particularly insidious: fraudsters build seemingly legitimate customer profiles over time, bypassing traditional fraud prevention controls until losses finally materialize.

Critically, every major fraud vector scored 4.3 or above out of 5 when organizations rated the impact of AI-enabled fraud, and all scores fell within a narrow 0.22-point range globally. This tight clustering signals something important: organizations are no longer facing one dominant fraud type. They are facing simultaneous, distributed attacks across every vector at once.

The Enterprise Fraud Challenge: A Gap Between Ambition and Execution

Nearly every organization surveyed (99%) recognizes the need for an enterprise-wide, unified approach to fraud prevention. Yet recognition and execution are not the same thing.

Only 28% of organizations have fully deployed AI/ML-driven fraud detection models operating at scale. At most, 34% have fully deployed any of the three core AI use cases surveyed: Gen AI for customer engagement support, Gen AI case management assistance, and AI/ML-driven detection models. The majority of financial institutions are still somewhere between piloting and limited production deployment.

This deployment gap is not primarily a technology problem. Nearly half of organizations (47%) cite difficulty integrating AI effectively as their primary challenge, ahead of increasing fraud complexity (40%), regulatory constraints (35%), data governance issues (34%), and legacy system limitations (32%).

Fraud modeling capabilities have frequently outpaced orchestration maturity. The result is that many organizations have advanced AI tools that are not yet meaningfully connected across channels, products, and portfolios. Without connected data, even the most sophisticated AI models cannot perform to their potential, allowing enterprise fraud to slip through the gaps.

False Positives: The Hidden Cost of Incomplete Fraud Prevention

The pressure to detect more fraud is creating a secondary problem: one in three organizations (33%) reports "high" or "very high" false positive rates from their fraud detection systems.

This matters because false positives are not just an operational inconvenience. When legitimate transactions are flagged as suspicious, customers face delays, extra verification steps, or declined services. That friction erodes trust, damages customer experience, and increases churn, particularly in rapidly expanding digital markets.

APAC records the highest overall "high/very high" false positive rate at 50%, with 20% reporting "very high", reflecting the pressures of rapid digital payments adoption outpacing control optimization. NORAM also reports high false positive rates (41% "high/very high") despite mature infrastructure — a sign that even well-funded environments struggle when fraud attempts become more sophisticated and targeted.

EMEA offers a contrasting benchmark: 44% of EMEA respondents report low false positives, ranking 20 percentage points above other regions, suggesting that regulatory alignment and more mature systems can help organizations strike a better balance between protection and customer experience.

The takeaway for fraud prevention leaders is clear: reducing false positives is not separate from stopping fraud. It is part of the same challenge. Adaptive, risk-based orchestration informed by better data signals, consortium intelligence, and continuous feedback loops is what allows organizations to tighten controls without creating friction that erodes the trust they've worked hard to build.

Orchestration Is the Keystone

If there is a single theme that runs through this research, it is this: the organizations that will win are those that move beyond fragmented, siloed fraud management to unified, AI-enabled orchestration.

Third-party vendor models are currently the most integrated and most influential in fraud decision-making — 41% of financial institutions say they have the greatest influence on their fraud decisions, compared to 28% for in-house models. This reflects the value of partnering with specialist vendors who can continuously tune models against evolving fraud typologies at scale and speed that in-house teams often cannot match.

But true resilience requires integrated decisioning frameworks that connect internal models, external vendor intelligence, and consortium data — in real time, across the full customer journey.

Virtually all organizations surveyed (99%) plan to leverage agentic AI in their fraud prevention strategy within the next 24 months — though expectations about the role it will play vary considerably, from core autonomous decision-making to an assisted, human-supervised function. EMEA shows the strongest confidence here, with 62% of organizations expecting agentic AI to play a core or autonomous role.

Where Investment Is Headed

Given all of the above, where are financial institutions prioritizing their fraud-related investment over the next 24 months?

Identity verification and authentication and AI/machine learning for fraud detection tie as the top investment areas, each cited by 16% of organizations as their largest share of investment. Real-time decision-making ranks second at 12%, followed by operational efficiency at 11%. The fact that investment priorities are closely clustered within just a 1.03-point range tells its own story: organizations recognize that there is no single silver bullet. Strengthening enterprise fraud resilience requires balancing multiple initiatives simultaneously.

Notably, minimizing customer friction ranks last among investment priorities, suggesting that as fraud grows in volume and complexity, organizations are prepared to accept some trade-off in customer experience in the near term to get protection right. The goal, however, must be to eventually eliminate that trade-off entirely through smarter orchestration.

The Path Forward: Four Priorities

The data in this research points to four immediate priorities for financial institutions:

First, close the deployment gap by moving AI from pilot to production at enterprise scale, prioritizing what works within disciplined fraud control frameworks rather than launching yet more pilots.
Second, invest in active orchestration across models, channels, products, and portfolios so that shared signals shape fraud management decisions in real time.
Third, apply risk-based, outcome-driven prioritization to balance competing investment demands, led by financial impact and regulatory exposure.
And fourth, treat fraud prevention and customer experience as a single, connected challenge. The organizations that can do both will be the ones that define competitive advantage in this era.

As Gregory Hodges, Head of Trust and Safety at JPMorgan Chase, explains: "AI has enabled bad actors to create attacks faster, at more scale, and with greater precision and quality. Attacks are becoming less manual and are more industrial, with bad actors also using AI to generate synthetic identities by combining real and fake data to create profiles that look, feel, and behave like real customers before eventually unleashing a broad-based fraud attack."

The threat is real, it is accelerating, and it is operating across every fraud vector simultaneously. But so is the opportunity for the organizations willing to move from fragmented pilots to true enterprise fraud transformation.

Want the full picture? Download the complete global survey report, "Fraud in the Age of AI: Trends, Threats, and Management Tactics," for in-depth data, regional breakdowns, and expert commentary from JPMorgan Chase, BNY, and LHV Bank. And watch for our regional eBooks — diving deeper into the specific fraud dynamics, AI deployment maturity, and investment priorities shaping fraud prevention in NORAM, EMEA, LATAM, and APAC.

[Download the Full Report]

Frequently Asked Questions

What is driving the recent surge in fraud attempts?
AI has become the primary accelerant, enabling fraudsters to create synthetic identities, execute deepfake-enabled account takeovers, and launch large-scale social engineering attacks with greater speed and precision than ever before.

If organizations know they need a unified fraud strategy, why haven't they implemented one?
The main barrier isn't technology, it's integration. Nearly half of organizations (47%) struggle to embed AI effectively within their existing fraud control frameworks, and many are still stuck between pilot programs and full production deployment.

What are false positives and why do they matter?
False positives occur when legitimate transactions are incorrectly flagged as fraudulent. Beyond being an operational headache, they create customer friction, damage trust, and increase churn, meaning poor fraud detection hurts the business even when no actual fraud occurs.

What is agentic AI and how does it fit into fraud prevention?
Agentic AI refers to AI systems capable of taking autonomous or semi-autonomous actions. In fraud prevention, it represents the next evolution of orchestration — with 99% of organizations planning to incorporate it within the next two years to help connect data, models, and decisions across the full customer journey in real-time.

Adam Davies

Adam Davies heads FICO's Fraud and Identity Product teams, bringing 28 years of extensive expertise in fraud, security, and financial crime prevention to his role. Throughout his 20-year tenure at FICO, he has established himself as an enterprise fraud thought leader with global experience gained through consulting engagements and solution implementations across more than 90 countries. Adam possesses a proven track record of guiding clients toward best practices in minimizing fraud exposure, centralizing fraud intelligence systems, and implementing customer-level fraud management strategies. Leveraging his comprehensive background, he drives innovation in FICO's product development, continuously enhancing capabilities for the company's global customer base while recently focusing on Generative AI applications to address emerging fraud management challenges.

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What Is Decision Optimization and How Can It Improve Results Across the Customer Lifecyle?

FICO — Mon, 14 Dec 2020 16:06:51 +0000

What Is Decision Optimization and How Can It Improve Results Across the Customer Lifecyle?

This blog post will walk you through how decision optimization works across the customer lifecycle and how to apply advanced analytics to determine decision strategies

Thu, 02/09/2023 - 07:09

Pawel Pasik

by FICO

Tue, 04/14/2026 - 10:05

Key Takeaways

Decision optimization uses prescriptive analytics and mathematical modeling to find the best possible solution to a complex problem, such as maximizing profit or minimizing risk, while respecting business constraints.
Decision optimization combines predictive modeling, action effect modeling, and business expertise to quantify the outcome of every possible decision and simulate how changing a strategy will affect KPIs.
Decision optimization can be applied at every stage of the customer lifecycle from acquisition and onboarding through customer management, retention and collections.
Real-world results speak for themselves. Home Credit International achieved a 26% increase in portfolio profit and a 29% increase in new sales using FICO's AI-powered optimization, and HSBC UK saw a 15% uplift in monthly card spend.
Decision optimization is becoming a core technology in financial services because it replaces gut-feel strategy changes with data-driven scenario simulation so organizations can test outcomes before committing to a new approach.

Decision optimization is the process of using prescriptive analytics and mathematical modeling to find the best possible solution to a complex problem given at least one objective, a set of constraints, and available data. It is widely used across industries to automate and improve logistically complex decisions like resource allocation, scheduling, routing, and planning.

Decision optimization is also becoming a core technology in financial services, because of its ability to deliver substantial increases in profit for specific strategies, driving the business forward in a systematic fashion.

Watch the short video below to hear Marc Drobe of FICO explains how decision optimization works.

As Mark explains, decision optimization can be described as a mathematical optimization process to develop better decision strategies. What we mean by better is that those strategies are more profitable, more manageable, and most importantly meet business goals and constraints.

What Are the Objectives of Decision Optimization?

The core objective of decision optimization is to find the best possible outcome to a scenario. That can mean maximizing a value (like profit, efficiency, or customer satisfaction) or minimizing a cost (like risk, waste, or time), while respecting all relevant constraints (resource limits, business rules, operational requirements, etc.).

Beyond finding an optimal solution, decision optimization also helps organizations understand why one solution is better than another (for example, why accepting a lower profit margin on one product might lead to better overall returns across a portfolio). It enables faster and more consistent decision-making at scale and allows organizations to test scenarios to see how outcomes would change.

Using decision optimization you can:

Define scenarios using objectives and constraints
Solve for the optimal strategy within those given constraints
Analyze expected effects on KPIs and drill into trade-offs
Choose the scenario you want to implement

Understanding the Role of Decision Impact Modeling in Decision Optimization

At the core of the optimization approach is decision impact modelling, which combines:

Predictive modelling: to estimate the probability of a customer behaving in a certain way, such as the likelihood of responding to an offer, or cancelling a service
Profit modelling: to quantify the financial outcome of each possible decision, including the revenues it could generate and the costs it would incur
Action-effect modelling: to understand how a specific action will shift behavior compared to taking no action, for example how much a discount increases the probability of a customer accepting an offer
Business expertise: to incorporate domain knowledge, strategic priorities, and judgment that data alone may not capture, so the decision model reflects real-world context and goals

How to Use Decision Optimization Across the Customer Lifecycle

Decision optimization is often used to derive the specific actions that should be taken to achieve a particular objective, such as raising profit by XX%, under user-defined constraints.

For example, a loan portfolio manager setting pricing might ask:

What is the impact on my portfolio if I change my strategy?
What are the possible points I can run my portfolio at and evaluate the tradeoffs between two competing objectives across all points to make the best decision?
What are the possible impacts if I want to increase both NPI and volume, even though they are competing objectives?
What is the impact on my KPIs if I move away from business as usual and maximize net interest income or origination volume?

Decision optimization can be used to improve strategies and outcomes across the customer lifecycle:

Customer acquisition

Identifying which prospects to target, with what offer, and through which channel
Maximizing the likelihood of conversion while staying within budgets

Customer Onboarding

Determining the right product configuration or service tier to assign to a new customer based on their predicted behavior and risk profile

Customer Management

Deciding which customers to proactively engage, with what product or service adjustment, and at what point in their journey
Optimizing for long-term value by balancing cross-sell and upsell opportunities against the risk of over-contacting or eroding trust

Customer Retention

Predicting which customers are at risk of leaving and determining the most cost-effective intervention, such as a targeted offer or outreach, to retain them

Expansion

Identifying which existing customers are most likely to benefit from, and accept, additional products or higher credit limits
Determining the right offer and timing

Advocacy

Identifying customers who have had consistently positive experiences and are most likely to refer others or act as brand advocates
Determining the right incentive or recognition to encourage advocacy

Decision Optimization in Action: Examples and Use Cases

A good example of decision optimization is action is Home Credit International’s loan pricing optimization project. In this post, Petr Kapoun, Erste Group Risk Management Advisor and former Chief Risk Officer at Home Credit Russia, explains how he has used FICO AI-powered optimization to achieve remarkable results: 26% increase in portfolio profit and a 29% increase in new sales. According to him, one of the key benefits of decision optimization is to use analytics, rather than hunches, to simulate outcomes of changing strategies.

How FICO Can Help You Optimize Decisions

Discover the optimization capabilities in FICO Platform
Explore FICO Xpress Optimization, the world-class mathematical optimization solver used for large-scale problems in a variety of industries such as banking, ecommerce, supply chain, logistics, manufacturing, and more.
Request a free trial license for Xpress to see its decision optimization capabilities for yourself
Download this case study to see how HSBC UK achieved a 15% increase in monthly spend thanks to Xpress
Read this case study to learn how Xpress helped Pepsico reduced daily production solve times by 30%

This is an update of a post from 2020.

Frequently Asked Questions

What is the difference between decision optimization and predictive analytics?

Predictive analytics estimates what is likely to happen, for example, the probability that a customer will default or churn. Decision optimization takes those predictions and determines the best action to take given those predictions, your business objectives, and your constraints. The two work together: better predictions lead to better optimization.

How long does it take to implement decision optimization?

Implementation timelines vary depending on the complexity of the problem, the availability of historical data, and the maturity of your existing analytics infrastructure. That said, platforms like FICO® Xpress are designed to accelerate optimization speed so organizations benefit from fast deployment and faster returns.

Do you need a data science team to use decision optimization?

While advanced optimization projects benefit from operations researchers and data scientists, modern decision optimization tools are increasingly accessible to business analysts and risk managers. FICO® Xpress, for instance, allows business users to define objectives, set constraints, and simulate scenarios without needing to write complex mathematical models from scratch.

How is decision optimization different from rules-based decisioning?

Rules-based decisioning applies fixed, conditional logic to decisions, for example, "approve if credit score is above X." Decision optimization, by contrast, dynamically determines the best action across a large number of variables and possible outcomes simultaneously, allowing organizations to balance competing objectives and adapt strategies as conditions change. Rules still play a role, but optimization determines the best configuration of rules to meet your business objectives.

FICO

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What Is Decision Optimization and How Can It Improve Results Across the Customer Lifecyle?

FICO — Mon, 14 Dec 2020 16:06:51 +0000

What Is Decision Optimization and How Can It Improve Results Across the Customer Lifecyle?

This blog post will walk you through how decision optimization works across the customer lifecycle and how to apply advanced analytics to determine decision strategies

Thu, 02/09/2023 - 07:09

Pawel Pasik

by FICO

Tue, 04/14/2026 - 10:05

Key Takeaways

Decision optimization uses prescriptive analytics and mathematical modeling to find the best possible solution to a complex problem, such as maximizing profit or minimizing risk, while respecting business constraints.
Decision optimization combines predictive modeling, action effect modeling, and business expertise to quantify the outcome of every possible decision and simulate how changing a strategy will affect KPIs.
Decision optimization can be applied at every stage of the customer lifecycle from acquisition and onboarding through customer management, retention and collections.
Real-world results speak for themselves. Home Credit International achieved a 26% increase in portfolio profit and a 29% increase in new sales using FICO's AI-powered optimization, and HSBC UK saw a 15% uplift in monthly card spend.
Decision optimization is becoming a core technology in financial services because it replaces gut-feel strategy changes with data-driven scenario simulation so organizations can test outcomes before committing to a new approach.

Watch the short video below to hear Marc Drobe of FICO explains how decision optimization works.

What Are the Objectives of Decision Optimization?

Using decision optimization you can:

Define scenarios using objectives and constraints
Solve for the optimal strategy within those given constraints
Analyze expected effects on KPIs and drill into trade-offs
Choose the scenario you want to implement

Understanding the Role of Decision Impact Modeling in Decision Optimization

At the core of the optimization approach is decision impact modelling, which combines:

Predictive modelling: to estimate the probability of a customer behaving in a certain way, such as the likelihood of responding to an offer, or cancelling a service
Profit modelling: to quantify the financial outcome of each possible decision, including the revenues it could generate and the costs it would incur
Action-effect modelling: to understand how a specific action will shift behavior compared to taking no action, for example how much a discount increases the probability of a customer accepting an offer
Business expertise: to incorporate domain knowledge, strategic priorities, and judgment that data alone may not capture, so the decision model reflects real-world context and goals

How to Use Decision Optimization Across the Customer Lifecycle

Decision optimization is often used to derive the specific actions that should be taken to achieve a particular objective, such as raising profit by XX%, under user-defined constraints.

For example, a loan portfolio manager setting pricing might ask:

What is the impact on my portfolio if I change my strategy?
What are the possible points I can run my portfolio at and evaluate the tradeoffs between two competing objectives across all points to make the best decision?
What are the possible impacts if I want to increase both NPI and volume, even though they are competing objectives?
What is the impact on my KPIs if I move away from business as usual and maximize net interest income or origination volume?

Decision optimization can be used to improve strategies and outcomes across the customer lifecycle:

Customer acquisition

Identifying which prospects to target, with what offer, and through which channel
Maximizing the likelihood of conversion while staying within budgets

Customer Onboarding

Determining the right product configuration or service tier to assign to a new customer based on their predicted behavior and risk profile

Customer Management

Deciding which customers to proactively engage, with what product or service adjustment, and at what point in their journey
Optimizing for long-term value by balancing cross-sell and upsell opportunities against the risk of over-contacting or eroding trust

Customer Retention

Predicting which customers are at risk of leaving and determining the most cost-effective intervention, such as a targeted offer or outreach, to retain them

Expansion

Identifying which existing customers are most likely to benefit from, and accept, additional products or higher credit limits
Determining the right offer and timing

Advocacy

Identifying customers who have had consistently positive experiences and are most likely to refer others or act as brand advocates
Determining the right incentive or recognition to encourage advocacy

Decision Optimization in Action: Examples and Use Cases

How FICO Can Help You Optimize Decisions

Discover the optimization capabilities in FICO Platform
Explore FICO Xpress Optimization, the world-class mathematical optimization solver used for large-scale problems in a variety of industries such as banking, ecommerce, supply chain, logistics, manufacturing, and more.
Request a free trial license for Xpress to see its decision optimization capabilities for yourself
Download this case study to see how HSBC UK achieved a 15% increase in monthly spend thanks to Xpress
Read this case study to learn how Xpress helped Pepsico reduced daily production solve times by 30%

This is an update of a post from 2020.

Frequently Asked Questions

What is the difference between decision optimization and predictive analytics?

How long does it take to implement decision optimization?

Do you need a data science team to use decision optimization?

How is decision optimization different from rules-based decisioning?

FICO

See all posts

Blog home

What Is Decision Optimization and How Can It Improve Results Across the Customer Lifecyle?

FICO — Mon, 14 Dec 2020 16:06:51 +0000

What Is Decision Optimization and How Can It Improve Results Across the Customer Lifecyle?

This blog post will walk you through how decision optimization works across the customer lifecycle and how to apply advanced analytics to determine decision strategies

Thu, 02/09/2023 - 07:09

Pawel Pasik

by FICO

Tue, 04/14/2026 - 10:05

Key Takeaways

Decision optimization uses prescriptive analytics and mathematical modeling to find the best possible solution to a complex problem, such as maximizing profit or minimizing risk, while respecting business constraints.
Decision optimization combines predictive modeling, action effect modeling, and business expertise to quantify the outcome of every possible decision and simulate how changing a strategy will affect KPIs.
Decision optimization can be applied at every stage of the customer lifecycle from acquisition and onboarding through customer management, retention and collections.
Real-world results speak for themselves. Home Credit International achieved a 26% increase in portfolio profit and a 29% increase in new sales using FICO's AI-powered optimization, and HSBC UK saw a 15% uplift in monthly card spend.
Decision optimization is becoming a core technology in financial services because it replaces gut-feel strategy changes with data-driven scenario simulation so organizations can test outcomes before committing to a new approach.

Watch the short video below to hear Marc Drobe of FICO explains how decision optimization works.

What Are the Objectives of Decision Optimization?

Using decision optimization you can:

Define scenarios using objectives and constraints
Solve for the optimal strategy within those given constraints
Analyze expected effects on KPIs and drill into trade-offs
Choose the scenario you want to implement

Understanding the Role of Decision Impact Modeling in Decision Optimization

At the core of the optimization approach is decision impact modelling, which combines:

Predictive modelling: to estimate the probability of a customer behaving in a certain way, such as the likelihood of responding to an offer, or cancelling a service
Profit modelling: to quantify the financial outcome of each possible decision, including the revenues it could generate and the costs it would incur
Action-effect modelling: to understand how a specific action will shift behavior compared to taking no action, for example how much a discount increases the probability of a customer accepting an offer
Business expertise: to incorporate domain knowledge, strategic priorities, and judgment that data alone may not capture, so the decision model reflects real-world context and goals

How to Use Decision Optimization Across the Customer Lifecycle

Decision optimization is often used to derive the specific actions that should be taken to achieve a particular objective, such as raising profit by XX%, under user-defined constraints.

For example, a loan portfolio manager setting pricing might ask:

What is the impact on my portfolio if I change my strategy?
What are the possible points I can run my portfolio at and evaluate the tradeoffs between two competing objectives across all points to make the best decision?
What are the possible impacts if I want to increase both NPI and volume, even though they are competing objectives?
What is the impact on my KPIs if I move away from business as usual and maximize net interest income or origination volume?

Decision optimization can be used to improve strategies and outcomes across the customer lifecycle:

Customer acquisition

Identifying which prospects to target, with what offer, and through which channel
Maximizing the likelihood of conversion while staying within budgets

Customer Onboarding

Determining the right product configuration or service tier to assign to a new customer based on their predicted behavior and risk profile

Customer Management

Deciding which customers to proactively engage, with what product or service adjustment, and at what point in their journey
Optimizing for long-term value by balancing cross-sell and upsell opportunities against the risk of over-contacting or eroding trust

Customer Retention

Predicting which customers are at risk of leaving and determining the most cost-effective intervention, such as a targeted offer or outreach, to retain them

Expansion

Identifying which existing customers are most likely to benefit from, and accept, additional products or higher credit limits
Determining the right offer and timing

Advocacy

Identifying customers who have had consistently positive experiences and are most likely to refer others or act as brand advocates
Determining the right incentive or recognition to encourage advocacy

Decision Optimization in Action: Examples and Use Cases

How FICO Can Help You Optimize Decisions

Discover the optimization capabilities in FICO Platform
Explore FICO Xpress Optimization, the world-class mathematical optimization solver used for large-scale problems in a variety of industries such as banking, ecommerce, supply chain, logistics, manufacturing, and more.
Request a free trial license for Xpress to see its decision optimization capabilities for yourself
Download this case study to see how HSBC UK achieved a 15% increase in monthly spend thanks to Xpress
Read this case study to learn how Xpress helped Pepsico reduced daily production solve times by 30%

This is an update of a post from 2020.

Frequently Asked Questions

What is the difference between decision optimization and predictive analytics?

How long does it take to implement decision optimization?

Do you need a data science team to use decision optimization?

How is decision optimization different from rules-based decisioning?

FICO

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FICO® Marketplace: A Faster Path to AI Outcomes

Jason Andrew — Fri, 27 Mar 2026 17:14:02 +0000

FICO® Marketplace: A Faster Path to AI Outcomes

A B2B exchange that empowers access to data, decision assets, and pre-built solutions

Mon, 03/30/2026 - 16:24

JenniferPiccinino@fico.com

by Jason Andrew

Chief Revenue Officer - Software

Mon, 03/30/2026 - 12:00

Technology leaders are chasing the same goal with AI: shorter time-to-value with lower integration risk. Decisioning platforms can do the heavy lifting, but they still need a steady supply of external intelligence — market data, customer behaviors, and other insights translated by trusted providers into fit-for-purpose products. These products can be data, APIs, machine learning models, analytics, and other offerings that can plug directly into live operational workflows.

A decision intelligence platform closes the gap between supply and demand by aligning marketplace products with the runtime that executes decisions. For example, it gives teams governed access to rich sets of third-party data that drive real outcomes.

FICO® Marketplace delivers a scalable and open approach to accessing intelligent products and decision assets in FICO® Platform. It enables a curated, industry-aware catalog of decision intelligence assets with faster activation, stronger governance, and a direct line to proven partner capabilities that are ready for enterprise use. FICO Marketplace providers include LexisNexis, iPacket, Prove Identity, Mitek, Pipl, and SentiLink to name a few.

A Governed Exchange for Data, Models, and Decisions

FICO Marketplace is a B2B, industry-focused digital exchange that connects teams to API-delivered data and deployable decision products — embedded into the existing business platform. It was designed to be simple and straightforward, giving enterprises a faster, safer path to reusable components that deliver measurable impact. It delivers a governed, scalable way to connect external intelligence to operational workflows.

This means speed where it counts because decision products arrive on demand to be authored into the operational use of cases that incorporate critical business decisions.

This means confidence because products can be trialed and simulated in FICO Platform to ensure the desired outcomes are achieved.

This means control by knowing where products are used, what versions are operating, and the customer decisions they are impacting.

Under the hood, FICO Marketplace builds the composability and integrated change pipeline in FICO Platform. This combination delivers faster innovation cycles for the entire ecosystem.

For customers, FICO Marketplace’s value shows up in four ways:

Shorter time-to-value: Integrations that take weeks or months can be completed in hours when teams start with pre-provisioned decision products and guided instructions.

Fit-for-purpose solutions: Asset-based, IP-rich products that are native to FICO Platform and cover a wide range of use cases across industries, including fraud management, originations, customer management, and marketing.

Reusability: Decision products can be used by anyone in the enterprise with authorized access to FICO Platform, breaking down silos, and improving collaboration.

Governance and compliance: Transparency in how decision assets are versioned and operated reduces risk across operational workflows.

Partners can manage their own catalog of products. The ease of listing purpose-built products with instant provisioning directly in FICO Platform reduces adoption barriers and accelerates time-to-revenue, as customers can quickly try and deploy these products.

Use Case: Embedding Reusable Fraud Controls

Managing fraud is an intricate and challenging problem. Controls must be attuned to products and channels with reliable access to trusted external data sources to prevent compromise.

FICO Marketplace simplifies fraud management by providing easy access to proven solutions. Consider enterprises such as LexisNexis Risk Solutions, which use FICO Marketplace as a gateway to deliver fraud prevention solutions to FICO Platform users. Customers can use signals to enrich identity and behavioral risk decisions, such as assessing email account takeover risk events to strengthen step-up logic without adding customer friction.

As Justin Tintzman, national account manager of integration and business services at LexisNexis Risk Solutions, said in SiliconANGLE, “We have a solution in fraud intelligence that can predict if an applicant is going to be fraudulent in six to 12 months, which is very powerful stuff. All that’s going to be available within FICO Marketplace.”

Nikhil Behl, president of software at FICO, describes similar benefits in other industries. “A large global bank bypassed a projected two-year, $1.5M internal effort by adopting partner assets natively through FICO Marketplace. One North American telco is currently experimenting with adding four to five new data sources per year, and our goal is to scale their productivity by at least 3x through automation and agentic workflows.”

See FICO Marketplace In Action

The following video demonstrates how FICO Marketplace operates natively within FICO Platform, with no separate onboarding required. Users can search for what they need, customize data service APIs, and communicate directly with partners for support.

What FICO Marketplace Unlocks for CTOs

The business case for customers centers on three questions:

How fast can we try and validate product lift in target use cases?

How quickly can we integrate, deploy, and scale the winners across all relevant use cases in the business?

How efficiently can we repeat this cycle to stay ahead?

FICO Marketplace answers production-grade workflows that move organizations from experimentation to outcomes faster, reduce integration time through FICO Platform, and provide a consistent operating model across use cases.

Platform teams should evaluate FICO Marketplace through the lens of operating leverage over time:

Start with a use case that has clear outcome metrics and select a marketplace product with strong signal coverage and value.

Use FICO Platform’s change pipeline to run-controlled trials, then promote the winner to targeted rollout.

Treat the value process as a continuous flywheel, not a one-off pilot.

The Need for Production-Relevant Data

Decisions only create value when they act on real signals. The value of FICO Marketplace remains unproven until relevant products are discovered and tested against production data to compare against historical outcomes.

This is easy to use with FICO Platform’s simulation capability to increase the relevance of evaluation and shorten the gap between promise and impact.

Turn Ecosystem Intelligence into Measurable Impact

The benefits are clear: for domains where the strongest signals often live outside organizational walls, FICO Marketplace accelerates access to intelligence while keeping operational control in business hands.

FICO Marketplace provides on-ramps, operational guardrails, and buyer-provider alignment, while FICO Platform provides runtime, telemetry, and lifecycle control. This combination — speed, control, and leverage — will define how AI investments into durable business outcomes across every business cycle.

Learn How FICO Platform Can Power Your Business:

Explore banking innovation with FICO Marketplace

Read more about applied intelligence with FICO Platform

See customers discuss their use of FICO Platform

Jason Andrew

Jason leads the Global Sales and Partner & Alliance teams at FICO, where he drives the transformation of the company’s global sales organization. Partnering closely with leadership, he focuses on enhancing customer engagement, improving productivity, and aligning teams with FICO’s long-term growth strategy. Before joining FICO, Jason was Chief Revenue Officer at cybersecurity company Trellix, overseeing a global team of 800+. He unified sales, pre-sales, customer success, and revenue operations to accelerate growth and scalability during a period of significant industry disruption. Earlier, Jason spent 25 years at BMC Software, where he held multiple leadership roles including more than five years as the company’s first Chief Revenue Officer. Leading over 1,500 people, he unified divisions into a high-performing sales organization and played a pivotal role in redefining BMC’s revenue model.

Jason began his career as a software engineer in New Zealand before expanding into marketing, enablement, and R&D. His leadership journey has taken him across Australia, New Zealand, the United Kingdom, and the United States, giving him a truly global perspective on technology and business. Passionate about creating customer-first organizations, Jason emphasizes building lasting partnerships, driving mutual growth, and cultivating strong sales cultures centered on people, development, and coaching. He and his wife Emma live in Austin, Texas, and have three adult children, two grandchildren, and three Cane Corso dogs.

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Enterprise Decisioning: The IT Trend That’s Transforming Banking

FICO — Thu, 30 Nov 2023 10:49:13 +0000

Enterprise Decisioning: The IT Trend That’s Transforming Banking

The drive to reduce technical debt from disconnected legacy systems is leading banks to enterprise decision platforms - with benefits for IT, the commercial team and consumers

Tue, 12/09/2025 - 15:08

Darcy Sullivan

by FICO

Mon, 03/30/2026 - 09:50

One top trend for banks in EMEA isn't covered in the media: the move to an enterprise approach to software and technology, to replace the siloed legacy systems used today.

The banking industry has talked about this for years. But it’s finally happening and is rapidly accelerating. And while its proponents have often been on the risk management, marketing and product management side of the house, the momentum now is being driven by forward-looking CIOs and CTOs. They have taken the lead because the advantages of an enterprise approach are so compelling — and the status quo is unsustainable.

Legacy Technology Is Choking IT and Strangling Growth

Legacy technology — the siloed systems behind every aspect of banking automation — carries with it enormous maintenance costs and so-called “technical debt.” I have spoken to banks with 15 systems for managing customers, each with its own data repository. This makes it nearly impossible to take true customer-focused decisions, because each system is constructing its own profile of the customer based on its own data.

These systems are a real burden on growth and profitability. Business owners will complain about the months it takes to spin up a new project or customer offer. This can take up to a year for some organizations, putting them in a distinctly uncompetitive position while they wait for IT. But from an IT perspective, the resources to serve every area of the business, to manage every system, to execute every change to strategy, simply aren’t there.

This is especially true given that a greater proportion of IT budgets are being spent simply on meeting new regulatory obligations. In the UK, Consumer Duty is a recent example of this – in an Ernst & Young survey, more than 85% of firms identified technology transformation as playing a role in meeting the requirements.

Moving to an Enterprise Platform Approach

This is why IT is championing a move to enterprise-wide decision platforms built on microservices and common capabilities that can be configured to rapidly meet new business needs. One of FICO’s customers, Bradesco, has leveraged the same set of microservices in FICO Platform to power more than 150 initiatives across the bank.

These systems also enable business owners to make changes to decision strategies without getting into an IT queue — a win for both sides of the equation. This also greatly reduces the time to make such changes, improving agility and competitiveness. A good example is Bank of Montreal, which has reported being able to deploy decision changes twice as fast as before.

Another advantage for IT is that they can distribute the costs of systems more effectively. In a transaction-based SaaS model, charges can be applied to the activity by the commercial team that generates revenue. This reduces the fixed costs of the business by switching fixed costs to variable costs, CapEx to RevEx, freeing up the CapEx budget for other priorities.

Benefits for All Parties

The impact of this enterprise transformation will be big not just for banks but for their customers:

IT teams becoming revenue makers and innovators vs cost centers, having more of a commercial impact. At an event last year, Gavin Reuben, Chief Information Officer, BluNova, said that by moving their use of FICO Platform from on-prem to cloud, he was able to stop focusing on software maintenance and focus instead on innovation and business transformation.
Moving to a shared set of tools and capabilities can bring significant savings in IT by moving away from various bespoke solutions requiring different training, maintenance, etc.
Business teams have more control over their decision strategies, which improves performance.
Customers will finally feel like the bank really knows them. By breaking down the compartmentalization we see today, banks can have the insight to make more consumer-specific decisions. The risk of not doing this is significant: a recent Gartner survey showed that brands risk losing more than 1 in 3 of their customers due to poor personalization.

Enterprise Transformation in 2026

Where will this go in 2026? All the signs are that it will continue apace, with laggards looking to jump on the trend as their competitors see the benefits spreading.

Banks that have started their platform journey for enterprise decisioning will rapidly spread the benefits from originations to marketing, customer management and collections. This will lead to better decisions at a lower cost. These organizations will be able to understand their customers’ needs, characteristics and priorities in much more depth, which creates greater clarity about what products to offer, and which communications and service processes will provide the best customer outcomes.

In the UK, this will provide clear advantages when it comes to meeting the Consumer Duty requirements. Achieving excellence in the Consumer Duty arena is expected to become a competitive differentiator.

While there are leaders and laggards within individual markets, there are also countries that are behind in taking this enterprise platform approach. FICO works with banks worldwide, and there is rapid adoption in the UK, the US, Canada and much of Latin America. Within EMEA, we have seen the greatest progress in the UK, in Turkey, in South Africa and in the Nordics. Banks in western Europe appear to be slower in taking up this approach, and 2026 will be critical for the early adopters.

Two UK banks that are far along this journey are Lloyds and Nationwide. Watch these videos to hear how their work is yielding a new approach to credit management:

The other change coming is the expansion of this approach into fraud management. To date, banks’ fraud teams have lagged the commercial and risk teams in this approach; they are still more reliant on point solutions, and have a high technical debt. We expect to see more movement in the next couple of years toward a more holistic approach that is better integrated with the rest of the business.

For example, we are now having more discussions with banks where application fraud is being discussed as part of the origination credit risk journey. Previously, originations and application fraud were managed by two teams with different systems; a platform approach could ensure more consistent decisions and better fraud identification to boot.

How an Enterprise Platform Approach Advances Your Digital Transformation

Read an article from The Journal of Digital Banking, “How Financial Services Leaders Are Using Enterprise Intelligence to Optimize Efficiency Ratios
Explore innovation with FICO Platform
See customers discuss their use of FICO Platform
Watch FICO Platform in action with the story of your everyday consumer, Digital Jane.

FICO

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Enterprise Decisioning: The IT Trend That’s Transforming Banking

FICO — Thu, 30 Nov 2023 10:49:13 +0000

Enterprise Decisioning: The IT Trend That’s Transforming Banking

The drive to reduce technical debt from disconnected legacy systems is leading banks to enterprise decision platforms - with benefits for IT, the commercial team and consumers

Tue, 12/09/2025 - 15:08

Darcy Sullivan

by FICO

Mon, 03/30/2026 - 09:50

One top trend for banks in EMEA isn't covered in the media: the move to an enterprise approach to software and technology, to replace the siloed legacy systems used today.

Legacy Technology Is Choking IT and Strangling Growth

Moving to an Enterprise Platform Approach

Benefits for All Parties

The impact of this enterprise transformation will be big not just for banks but for their customers:

IT teams becoming revenue makers and innovators vs cost centers, having more of a commercial impact. At an event last year, Gavin Reuben, Chief Information Officer, BluNova, said that by moving their use of FICO Platform from on-prem to cloud, he was able to stop focusing on software maintenance and focus instead on innovation and business transformation.
Moving to a shared set of tools and capabilities can bring significant savings in IT by moving away from various bespoke solutions requiring different training, maintenance, etc.
Business teams have more control over their decision strategies, which improves performance.
Customers will finally feel like the bank really knows them. By breaking down the compartmentalization we see today, banks can have the insight to make more consumer-specific decisions. The risk of not doing this is significant: a recent Gartner survey showed that brands risk losing more than 1 in 3 of their customers due to poor personalization.

Enterprise Transformation in 2026

Where will this go in 2026? All the signs are that it will continue apace, with laggards looking to jump on the trend as their competitors see the benefits spreading.

Two UK banks that are far along this journey are Lloyds and Nationwide. Watch these videos to hear how their work is yielding a new approach to credit management:

How an Enterprise Platform Approach Advances Your Digital Transformation

Read an article from The Journal of Digital Banking, “How Financial Services Leaders Are Using Enterprise Intelligence to Optimize Efficiency Ratios
Explore innovation with FICO Platform
See customers discuss their use of FICO Platform
Watch FICO Platform in action with the story of your everyday consumer, Digital Jane.

FICO

See all posts

Blog home

Enterprise Decisioning: The IT Trend That’s Transforming Banking

FICO — Thu, 30 Nov 2023 10:49:13 +0000

Enterprise Decisioning: The IT Trend That’s Transforming Banking

The drive to reduce technical debt from disconnected legacy systems is leading banks to enterprise decision platforms - with benefits for IT, the commercial team and consumers

Tue, 12/09/2025 - 15:08

Darcy Sullivan

by FICO

Mon, 03/30/2026 - 09:50

One top trend for banks in EMEA isn't covered in the media: the move to an enterprise approach to software and technology, to replace the siloed legacy systems used today.

Legacy Technology Is Choking IT and Strangling Growth

Moving to an Enterprise Platform Approach

Benefits for All Parties

The impact of this enterprise transformation will be big not just for banks but for their customers:

IT teams becoming revenue makers and innovators vs cost centers, having more of a commercial impact. At an event last year, Gavin Reuben, Chief Information Officer, BluNova, said that by moving their use of FICO Platform from on-prem to cloud, he was able to stop focusing on software maintenance and focus instead on innovation and business transformation.
Moving to a shared set of tools and capabilities can bring significant savings in IT by moving away from various bespoke solutions requiring different training, maintenance, etc.
Business teams have more control over their decision strategies, which improves performance.
Customers will finally feel like the bank really knows them. By breaking down the compartmentalization we see today, banks can have the insight to make more consumer-specific decisions. The risk of not doing this is significant: a recent Gartner survey showed that brands risk losing more than 1 in 3 of their customers due to poor personalization.

Enterprise Transformation in 2026

Where will this go in 2026? All the signs are that it will continue apace, with laggards looking to jump on the trend as their competitors see the benefits spreading.

Two UK banks that are far along this journey are Lloyds and Nationwide. Watch these videos to hear how their work is yielding a new approach to credit management:

How an Enterprise Platform Approach Advances Your Digital Transformation

Read an article from The Journal of Digital Banking, “How Financial Services Leaders Are Using Enterprise Intelligence to Optimize Efficiency Ratios
Explore innovation with FICO Platform
See customers discuss their use of FICO Platform
Watch FICO Platform in action with the story of your everyday consumer, Digital Jane.

FICO

See all posts

Blog home

The Hidden Costs of Friction: What 18,000 Consumers Revealed About Identity Verification and Account Origination

Adam Davies — Fri, 27 Mar 2026 11:13:21 +0000

The Hidden Costs of Friction: What 18,000 Consumers Revealed About Identity Verification and Account Origination

Our comprehensive survey uncovered striking regional differences in consumer priorities and behaviors, challenging conventional wisdom about what drives account selection

Wed, 03/04/2026 - 16:03

ElleNelson@fico.com

by Adam Davies

Vice President, Product Management

Fri, 03/27/2026 - 11:15

In today's digital-first financial landscape, the balance between security and customer experience has never been more critical—or more delicate. A new FICO global consumer survey reveals surprising insights about how identity verification processes can create expensive customer churn, costing financial institutions both revenue and relationships in ways many organizations may not fully recognize.

The Global Consumer Perspective: Security vs. Simplicity

Our comprehensive study surveyed approximately 18,000 consumers across 18 countries spanning APAC, EMEA, Latin America, and North America, uncovering striking regional differences in consumer priorities and behaviors. The findings challenge conventional wisdom about what drives account selection and reveal concerning trends about customer abandonment.

When choosing financial accounts, consumers consistently prioritize three key factors: convenience, protection, and perceived value. However, the weight given to each varies dramatically by region. While customers in APAC prioritize ease of use above all else, consumers in other regions place stronger emphasis on fraud protection as their primary deciding factor. This regional disparity has profound implications for how financial institutions should tailor their onboarding strategies.

Customers want protection, simplicity, and value led by:

Ease of use: simple onboarding and effortless navigation
Good fraud protection: strong safeguards against fraud and unauthorized access
Good value: better benefits, lower fees, and fair pricing

The Account Opening Opportunity Hiding in Plain Sight

Perhaps most intriguing is what our research reveals about customer abandonment during the account opening process. The data shows that complex identity verification procedures are creating significant friction at the worst possible moment—when potential customers are deciding whether to trust an institution with their financial relationship.

The abandonment rates vary considerably across different types of accounts and regions, with some account types experiencing particularly high drop-off rates. Personal bank accounts, representing the foundation of most customer relationships, show concerning abandonment patterns that should give every financial services executive pause.

But the problem doesn't end at account opening. Our research uncovered an equally troubling trend: customers who successfully navigate initial identity checks may later reduce or completely stop using their accounts if they perceive ongoing verification processes as too burdensome. There is tremendous opportunity for forward-thinking banks to win, retain, and protect customers with streamlined, safe, and intuitive account opening.

Financial Crime Fears Drive Contradictory Behaviors

Consumer attitudes toward financial crime reveal a complex psychology that institutions must navigate carefully. Customers fear financial crimes that can strike suddenly and cause direct financial loss that's difficult to reverse. Yet their concerns vary significantly by region.

Scams top the worry list in APAC at 33%, while identity fraud dominates concerns in LAC at 37%. Meanwhile, card misuse ranks highest among North American consumers at 23%. These regional differences suggest that one-size-fits-all approaches to security messaging and verification processes may be missing the mark.

Adding another layer of complexity, our research reveals that 7% of all global respondents believe their identity has definitely been used to open a financial account fraudulently, with the highest percentage occurring in North America at 9%. This statistic underscores why consumers demand robust protection—even as they simultaneously abandon accounts due to verification friction.

Application Fraud: Where Friction and Risk Collide

The consumer behaviors uncovered in our research play out against a backdrop of increasingly sophisticated application fraud. When individuals misrepresent their identity, inflate their income, or use stolen credentials to open accounts, they exploit the very same onboarding processes that frustrate legitimate customers. The irony is sharp: the friction that drives good customers away often fails to stop determined fraudsters.

Application fraud is multifaceted. Third-party fraud uses stolen or synthetic identities and is the variety most consumers picture when they think about identity theft. But our survey findings on income exaggeration hint at a murkier reality — first-party fraud, where applicants deliberately falsify their own information, is deeply intertwined with the "situational flexibility" attitudes our respondents revealed. And mule accounts, opened to move illicit funds, can slip through onboarding checks that prioritize speed over scrutiny.

Layering on more verification may catch more fraud, but our data shows it also pushes legitimate customers toward abandonment. Streamlining without intelligent risk assessment simply opens the door wider for bad actors. The institutions getting this right are moving toward orchestrated and adaptive approaches that calibrate friction to risk in real time — lighter-touch verification for low-risk applicants, escalated scrutiny where signals warrant it. Done well, this strengthens fraud prevention and customer experience simultaneously.

The Biometric Authentication Puzzle

Consumer attitudes toward biometric authentication present another fascinating paradox. While these technologies promise to reduce friction while maintaining security, our research reveals significant gaps between user preference and perceived protection across different biometric methods.

The data shows that consumer acceptance varies not just by technology type, but also by region, suggesting that deployment strategies for biometric authentication need to consider local preferences and cultural attitudes toward privacy and technology adoption.

The Trust vs. Convenience Dilemma

Perhaps most intriguingly, our survey uncovered attitudes toward income exaggeration that reveal the complex relationship between consumer behavior and institutional risk management. The findings show that consumer tolerance for "situational flexibility" varies dramatically across different types of applications and regional contexts.

These insights have profound implications for first-party fraud prevention, regulatory compliance, and the design of application processes that accurately assess consumer ability to repay while maintaining reasonable friction levels.

What This Means for Financial Institutions

The research reveals a fundamental challenge: consumers want both bulletproof security and frictionless experiences, but current approaches often force an either-or choice. The institutions that will thrive are those that can thread this needle—delivering robust fraud protection through intelligent, adaptive processes that feel seamless to legitimate customers.

The regional variations in consumer behavior and preferences also suggest that global financial institutions need more nuanced, localized approaches to identity verification and fraud prevention. What works in North America may drive abandonment in APAC, and vice versa.

The Path Forward

The full implications of these findings extend far beyond simple customer satisfaction metrics. They touch on fundamental questions of revenue optimization, risk management, regulatory compliance, and competitive positioning in an increasingly digital marketplace.

The complete research reveals specific strategies and technologies that can help institutions navigate these challenges while providing detailed benchmarks for understanding how your organization's performance compares to regional and global standards.

The future of financial services belongs to institutions that can master the art of invisible security—protection so intelligent and seamless that customers feel both safe and unencumbered. The question is: will your organization be among them?

Dive Deeper Into the Data

Download the full consumer insights infographic to explore detailed findings across APAC, EMEA, LAC, and NORAM, including specific metrics, regional comparisons, and strategic recommendations for balancing security with customer experience.

How FICO Can Help You Stop Fraud

Understand more about modern approaches to enterprise fraud with the six E’s of enterprise fraud
Explore our fraud solutions
Explore our origination solutions

Adam Davies

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The Hidden Costs of Friction: What 18,000 Consumers Revealed About Identity Verification and Account Origination

Adam Davies — Fri, 27 Mar 2026 11:13:21 +0000

The Hidden Costs of Friction: What 18,000 Consumers Revealed About Identity Verification and Account Origination

Our comprehensive survey uncovered striking regional differences in consumer priorities and behaviors, challenging conventional wisdom about what drives account selection

Wed, 03/04/2026 - 16:03

ElleNelson@fico.com

by Adam Davies

Vice President, Product Management

Fri, 03/27/2026 - 11:15

The Global Consumer Perspective: Security vs. Simplicity

Customers want protection, simplicity, and value led by:

Ease of use: simple onboarding and effortless navigation
Good fraud protection: strong safeguards against fraud and unauthorized access
Good value: better benefits, lower fees, and fair pricing

The Account Opening Opportunity Hiding in Plain Sight

Financial Crime Fears Drive Contradictory Behaviors

Application Fraud: Where Friction and Risk Collide

The Biometric Authentication Puzzle

The Trust vs. Convenience Dilemma

What This Means for Financial Institutions

The Path Forward

Dive Deeper Into the Data

How FICO Can Help You Stop Fraud

Understand more about modern approaches to enterprise fraud with the six E’s of enterprise fraud
Explore our fraud solutions
Explore our origination solutions

Adam Davies

See all posts

Blog home