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.

Overview

Businesses across many industries spend millions of dollars employing advanced analytics to manage and improve their supply chains. Organizations look to analytics to help with sourcing raw materials more efficiently, improving manufacturing productivity, optimizing inventory, minimizing distribution cost and other related objectives. But the results can be less than satisfactory. It often takes too long to source the data, build the models and deliver the analyticsbased solutions to the multitude of decision-makers in an organization. Sometimes key steps in the process are omitted completely. In other words, the solution for improving the supply chain, i.e., advanced analytics, suffers from the same problems that it aims to solve.

For Industries: 
Banking
Overview

Are you prepared for IFRS 9? This major accounting standard change has had the attention of major banks for several years but is an equally high priority for auto finance providers as well.

For Industries: 
Banking
Overview

Some of the biggest challenges facing businesses worldwide relate to security. Increasingly, the domains of fraud protection, cybersecurity and regulatory compliance are merging within institutions, which are taking a more holistic view of financial crime. This is critical, as these areas share a need for rapid action based on real-time threat assessment. With this in mind, we asked four of FICO’s experts in this area to provide their predictions for the year ahead. 

Overview

With the term “Big Data” now commonplace, there’s no mistaking that today’s volume, variety and velocity of data challenges organizations that follow splintered approaches to data connection, ingestion, processing and analysis. Too many IT departments are still falling short of transcending legacy approaches that limit the value they derive from data. Typically, those organizations find themselves caught in differentiating data treatment by type — particularly between batch and streaming data — and consequently supporting disparate IT infrastructures. That seriously misaligns those organizations with contemporary data realities, and it short-circuits opportunity. 

For Industries: 
Banking
Overview

Credit markets have seen many changes in recent years, including tremendous innovation, the rise of marketplace lenders around the globe and new credit products from major technology players such as Amazon. That innovation is driving existing lenders to rethink their infrastructure and processes to become more nimble while regulators begin focusing on the new entrants (e.g., The US Treasury Department recently issued a whitepaper on potential disparate impact and fair lending for marketplace lenders). These changes require traditional firms to be agile and adapt quickly to new innovation, new players and new regulation, all the while improving the customer experience. 

Tim Van Tassel, Vice President and General Manager of FICO’s Credit Risk practice, weighs in on the subject of how marketplace lending is changing the credit landscape, and what that means for both new marketplace lenders and traditional credit-grantors. 

Overview

Companies are increasingly turning to data and analytics for competitive advantage. But more than 70% of enterprises report struggling to achieve consistently positive results despite spending more to build, maintain and analyze ever-larger data repositories.1 This may be because they are using the wrong approach. Rather than start with data, companies should apply a decision-first approach that begins with the business objective and decision model before considering data and analytic requirements. FICO® DMN Modeler and FICO® Decision Management Suite make creating, managing and executing clear decision models faster and easier than ever before. 

Overview

Strategic decisions drive success and the process of managing, automating and improving these decisions has become crucial for companies across all industries. Decision analysis techniques help companies make smart, timely decisions and decision trees are a good example of technology that can help determine the best decision. But the technology is just one of the critical elements of a decision strategy. You have to know how to think about the problem you’re trying to solve and how to design an effective strategy. And once you have a solid strategy in place, it’s important to continue taking a fresh look at your data, your rules and your strategies on a regular basis. You can use data, tools and analytic techniques to understand current results then redevelop strategies for continual improvement. 

For Industries: 
Banking, Insurance
Overview

Regardless of size, all financial institutions must know their exposure to money laundering/terrorist financing risk — not only organizationally, but customer by customer — and take risk-appropriate measures to mitigate it. The same goes for credit unions, life insurance companies, money service providers, casinos and other industries. As regulators (and criminals) expand their focus beyond top-tier banks, an increasing range of businesses are faced with the need to perform KYC (Know Your Customer) with the same precision as the best and largest institutions — but without the large investments. 

Overview

The increasing number, variety, speed and severity of cyber attacks calls for a new line of defense. While there are many signature-based solutions for protecting against known cyber attack vectors, the key gap is identifying threats for which no signature has yet been isolated. Better defenses are also needed to protect against attacks involving credential changes after spear phishing. To minimize losses, we must detect and stop threats based on the abnormal behaviors they exhibit in the network, as they occur. To prevent losses, we must predict and stop as many threats as possible during the reconnaissance period, before data is exfiltrated.

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