For Industries: 
Banking
Overview

The past few months have seen the launch of new real-time payment schemes in significant geographies. In November 2017, SEPA Credit Transfer Instant was launched in the eurozone, while in the USA The Clearing House Faster Payments scheme also went live. In February 2018, the newest real-time payments scheme was launched in Australia with their New Payments Platform. Meanwhile, Canada is on track to launch their real-time payments scheme by the end of 2019. These schemes are all intended for mass adoption. They will fundamentally alter how payments are made in the geographies concerned. This will have a significant impact on the types and volumes of fraud they experience.

In this whitepaper, we look at the history and experience of countries such as the UK that have had mass adoption of real-time payment schemes for some time. We will consider the effect that real-time payments has on fraud and the response of the public and the industry. We will ask what can be done to help manage fraud in the world of real-time payments and uncover the strategies that will help new adopters avoid the pitfalls experienced by the real-time payments pioneers.

Overview

Machine-learning (ML) models — compared to strictly additive models — can provide notable predictive lift when data presents complex relationships. However, without an understanding of the relationships captured by the ML model, we risk encoding accidental, unintentional and even undesirable features into these predictions. These surprising relationships may be introduced by unexpected biases in our data- collection methods, or by confounding treatments in our historical practices, which, if undetected, could yield models that are unfit for their intended tasks. On the bright side, however, revelations from an ML model’s content can inspire greater insights for the model creators. They may also foster greater trust among its users. This paper seeks to explore, illustrate and compare Explainable Artificial Intelligence (xAI) techniques that can help us gain deeper insights from ML models and operationalize them with far greater confidence. Specifically, we outline some of the explainability support for machine learning provided by toolsets available from FICO.

Overview

Business rules are an essential technology that can deliver measurable bottom line and other benefits. The true value of rules, however, is realized when you take a “decision-first” approach to enhancing business performance.
 
Based on decades of experience developing decision management applications, FICO has developed 12 steps to help you make the most of decision rules. Download this white paper now and learn how to:

  • Choose the right application and development approach
  • Write rules as effectively as possible
  • Ensure the right rules are written and that they have the expected impact
  • Operationalize analytics and increase the impact of decisions throughout the enterprise

 

Overview

All available industry statistics show that cybersecurity attacks and breaches continue to rise. The latest FICO survey results show that enterprise organizations are – quite rightly – prepared to invest more funds in cybersecurity technology and training requirements. Ovum's own security forecast supports this position, demonstrating that spending on security products and services will continue to rise at a compound annual growth rate (CAGR) of around 10% through to 2022. However, and despite evidence to the contrary, senior management continue to believe they are well positioned for cyber-readiness, and that their position will improve further in the year ahead.

For Industries: 
Retail, Banking, Agencies
Overview

Creditors and debt collectors have navigated substantial regulatory change for decades. Today’s outlook is full of uncertainties, including the prospect of increasingly complex and fragmented rules if the trend toward increased state regulation of debt collection and recovery continues.

In this environment, an important capability to look for in debt management systems is ease of configuration. Organizations collecting and recovering debt should be able to quickly and efficiently reconfigure any aspect of their collection and recovery process to fit evolving regulatory requirements. Further, a complete, transparent and detailed audit trail of every action taken should be in place with easy access and reporting capabilities.

This paper focuses on the essential components of a configurable, compliant debt management system.

For Industries: 
Banking
Overview

Access to credit plays a vital role supporting consumer expenditure, which makes up around 70% of the economic activity in a modern consumer-driven economy. By just about any measurement, the modern consumer finance business can be seen as a success, but that success has come with the consequence of rising levels of consumer indebtedness.

For Industries: 
Technology
Overview

Digital transformation (DX) is the act of leveraging technology to improve our knowledge, comprehension, decisions and actions. Within the next five years, it is widely expected that large enterprises will see the majority of their revenue driven by products and services that rely on digital technologies. Digital technologies continue to evolve. We have already seen the impact that big data, the cloud, social business and mobility are having on business. Add to that predictive and prescriptive analytics and cognitive capabilities, including machine learning (ML) and artificial intelligence (AI), and the stage is set for massive disruption over the next five years as enterprises apply digital technologies to transform their businesses. However, digital technologies all share the same DNA. This DNA consists of five core constructs: data (including events), decisioning, actions, analytics and improvements (learning). This white paper examines these five constructs but especially focuses on decisioning because of its central role in orchestrating the activities of these constructs.

For Industries: 
Banking
Overview

IDC surveyed 500 business executives, data scientists, statisticians and senior IT staff on their use of analytics and related capabilities across their enterprises. This InfoBrief delves into how organizations are deploying “decision-centric” capabilities to help power digital transformation (DX) initiatives. In addition to analytics, the results explain the current state of rules and optimization implementations, as well as measuring how businesses that leverage all three core capabilities have a clear decision advantage over their competitors.

Overview

It’s estimated that 30 million people in the US alone have one or more debts in collections, and household debt is on the rise. Significant 90-day delinquencies come from credit cards, mortgages (plus associated lines of credit), student loans, healthcare and auto loans. While the collection industry is drawn by the significant activity of these potential revenue streams, we should recognize conflicting trends from declining collection rates, increasing regulation and growing automation.

Overview

The FICO® Decision Management Suite is a complete cloud or on-premises solution that delivers an integrated suite of tools for creating fully customizable analytically powered applications. This document provides a high-level overview of the Decision Management Suite architecture and the elements of a decision management solution. It also provides an architectural overview of the FICO® Decision Management Platform, which provides a standardized set of analytic and execution services that power the Suite.

Using this document as a guide, the reader will understand the context and boundaries to guide architectural decision-making.

Want to take your business to new heights?

Request more information. Enter your information and we will respond directly to you.