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.

Overview

Over the past 40 years, business thought leaders in the industrialized world have had a love-hate relationship with artificial intelligence technologies. A succession of brief vogues, in which technology vendors sought to apply AI to a range of pragmatic business challenges, have been punctuated by periods of disillusionment.

More recently, however, AI and machine learning have emerged as powerful, practical and well-accepted tools for a wide range of business applications. The techniques have become vastly more capable. Equally important has been the explosion of capacity in information infrastructures, especially the development of databases and processing to manage “Big Data” during the first decade of the 21st century.

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

Competing on More than Analytics: Decision Solutions for Effective Customer Life-Cycle Management

Even though the basic processes of acquiring, retaining, and growing customers are as old as commerce itself, everything about these processes has changed.  Most executives value the use of analytics and data-driven decision making.  However, many organizations fall short in maximizing the value of data they already own or could acquire in deriving insights to support or automate decision making to maximize customer lifetime value. 

Download the IDC white paper “Competing on More than Analytics: Decision Solutions for Effective Customer Life-Cycle Management” and learn more about:

• How deploying decision solutions, which combine advanced analytics, rules, and optimization capabilities, can help businesses analyze and optimize the best decisions to make and actions to take
• Essential customer life-cycle management process steps, and how decision solutions support each step in the process
• Common characteristics of organizations that are using decision solutions for customer life-cycle management
• The future outlook and technological advances that will continue to enhance customer life-cycle capabilities
• Recommendations to address current challenges and drive new opportunities 

Overview

Cybersecurity threats and the number of reported breaches continue to rise, and if the last decade has taught us anything about the world of cyberthreats, it is that throwing more technology at the problem each time something new happens does little to improve things. Despite the proliferation of new, tested, and threat-specific technologies that have been brought to market, we have not seen the problems reducing or going away. In fact, it is widely acknowledged that the number of attacks, breaches, and subsequent business and data losses are growing exponentially worse with each passing year.

What is needed is an across-business approach to cybersecurity that involves business-focused protection systems and the skills and levels of authority needed to support the technology. This report looks at what organizations are doing to improve their situations and identifies areas where improvements are needed.

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.

Overview

To prevent financial crime and terrorist financing, organizations of all types have joined the global fight to stop criminals from realizing the financial benefits of their crimes. Compliance with regulatory obligations for anti-money laundering (AML) and terrorist financing is not easy; it is difficult to stay ahead of changing regulations and deploy appropriate know your customer (KYC) and AML processes across multiple countries. The FICO® TONBELLER® Siron® suite offers flexible and configurable modules and brings advanced analytics and artificial intelligence to AML compliance. This helps you to manage the end-to-end AML process and identify, prioritize and manage cases appropriately.

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

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.