Overview

Cybersecurity strategies often consist of “whack-a-mole” exercises focused on the perpetual detection and mitigation of vulnerabilities. As a result, organizations must re-think the ever-escalating costs associated with vulnerability management. After all, the daily flow of cybersecurity incidents and publicized data breaches, across all industries, calls into question the feasibility of achieving and maintaining a fully effective defense. The time is right to review the risk management and risk quantification methods applied in other disciplines to determine their applicability to cybersecurity. These proactive and systematic approaches may provide better quantification of the effectiveness of cybersecurity management practices.

The banking industry, as an example, bears similar risks in its management of credit card risk and has a long history of successfully applying predictive analytics and statistical methods to effectively identify, quantify and predict these risks. Forewarned is, after all, forearmed. If these predictive analytics could be used to harness the risk of data breaches, the damages (both financial and reputational) could be reduced or avoided by a data-driven organization. Similar large-scale data analysis and modeling techniques are commonly used to underwrite property and casualty insurance or assess credit or interest rate risk. In this paper we will explore the potential of forecasting cybersecurity risk with a detailed explanation of the underlying technologies and analytics.

For Industries: 
Banking
Overview

The traditional process of banking or getting a loan used to involve walking into the neighborhood financial institution, sitting down with a customer service representative, filling out forms and discussing needs. Then, maybe a few days later, the customer could walk out with a check. The process is drastically different today, as the financial services industry has undergone a sea of changes. Today, if a person lives in New Jersey, it is perfectly normal to hold a bank account in, say, their home town of Austin, Texas. There is little reason to have a local bank account as several banking tasks can be done remotely. A customer can deposit a check via mobile banking, withdraw cash at an ATM or apply online for a loan.

Overview

In a world in which Big Data is becoming more prevalent and relevant to everyday business operations, systems to help understand the data and manage decisions are ever more crucial. Decision makers at every level must be able to utilize relevant information in the moment to optimize the outcome of their decisions.

The FICO Decision Management Suite (DM Suite) provides an easy way for customers to customize, deploy, and scale state-of-the-art advanced analytics and decision management solutions. It allows clients to quickly integrate FICO and FICO partner decisioning tools and technologies with their own IT infrastructure, helping organizations of all sizes realize the promise of advanced analytics and decision management via cost effective, scalable cloud and on-premises solutions. The Decision Management Platform (DMP) is a core component that underlies DMS, and will be the focus of this paper.

The extent to which this platform works quickly and appropriately to address an organization’s needs depends on its ability to process the necessary amount of data quickly. This paper explores how to maximize the horizontal and vertical scalability of FICO Decision Management Platform (DMP), within the DM Suite, and explains how it processes data efficiently to drive informed decision making in both online transaction and batch processing jobs.

Overview

We live and work in a world increasingly defined by data, analytics and digital platforms. Companies aggressively invest in these technologies knowing they are critical to better performance and competitive advantage. Yet many companies struggle to achieve consistently positive results from their data and analytics initiatives. According to Forrester Research, 73% of enterprise architects aspire to help their firms be data-driven enterprises, but only 29% say they are good at translating analytics into action.

For Industries: 
Banking
Overview

Supercharge your deposit pricing with five key capabilities

Even as interest rates increase, there’s more to deposit pricing than customer price sensitivity. Deposit behaviors are more sophisticated to analyze than many businesses think, while pricing solutions need to be examined under longer time period than are usually allocated. Taking deposit pricing to the next level requires a more thoughtful approach, leveraging the latest in analytic and technological innovations.
 
In this white paper, FICO Deposit Practice Leader Ashwin Gurnani shares five ways that deposit teams can ramp up their deposit pricing capabilities, including:

  • Re-engineering your data and modeling strategy to support your entire deposit portfolio
  • Implementing a technology-based forecasting and what-if tool
  • Using optimization to help account for your customer/business constraints and objectives
For Industries: 
Capital Markets, Banking, Agencies, Insurance
Overview

Artificial intelligence (AI) and machine learning are big buzzwords at present — there doesn’t seem to be a business problem where they are not being applied. Now AI has come to the world of anti–money laundering compliance.

More firms are boasting about the AI capabilities of their software, but quite often we’re left thinking, “So what?” It all sounds very clever but how does it solve our very real business issues?

To help you understand how AI could ease your AML headaches, we’ve brought together three of our experts to shine a light on this important but often confusing subject.

Overview

Ask any consumer why they prefer and remain loyal to a particular brand or company and, not surprisingly, two primary reasons most often surface: Customers like companies that offer choices in products and services, so they can make acquisitions suited to their individual tastes and needs; additionally, customers are quick to remember their experience in dealing with a company, good or bad. Still, these responses don’t fully explain a consumer’s loyalty to one company over another—after all, many businesses offer a variety of choice, and all strive to provide superlative customer experience. What consumers usually don’t see (nor need to see) is how some businesses—the ones they prefer—apply technology to surpass others in solving for what is referred to as “business complexity.”

For Industries: 
Banking
Overview

Credit scoring provides a single number representing the likelihood an applicant will become seriously delinquent. Having this single number improves origination efficiency since you can rank-order applicants by risk level and set cutoffs to take certain actions when scores fall above or below them. A small business scorecard predicts the creditworthiness of the business in the same way a consumer scorecard predicts it for the business principal. Having this additional meaningful number—from analysis of both business and consumer data— sharpens separation of good and bad credit risk. Scores also improve decision fairness—and make it easy to prove.

For Industries: 
Banking
Overview

Decision acceleration is about using technology to assess risk and process credit applications faster. An origination system does this by following your policy rules to make automated approve, decline or send-for-review decisions. It examines applicant data and scores, then takes the actions you want it to take based on your criteria and cutoffs. This enables you to grow volume by auto-decisioning more small business applications, while providing your small business experts with the information they need for applications requiring their attention.

For Industries: 
Banking
Overview

Background: The National Consumer Assistance Plan (NCAP) is a comprehensive series of initiatives intended to evaluate the accuracy of credit reports, the process of dealing with credit information and consumer transparency. As part of NCAP, the consumer reporting agencies (CRAs) — Experian®, Equifax® and TransUnion® — are changing the data standards and service level requirements of the public record data they maintain.

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