For Industries: 
Banking
Overview

Streamline your merchant onboarding process and strengthen risk management with a single, extensible platform

FICO® Merchant Onboarding Solution streamlines traditionally cumbersome business processes and sharpens compliance, fraud and credit risk evaluations with cutting- edge analytics and decisioning capabilities. Acquirers are empowered to design workflows—seamlessly weaving together automated and manual processes—to reduce onboarding times from days to minutes. Intelligent, easy-to-use dashboards and KPIs provide business and regulatory insights that facilitate exploring what-if scenarios and impact testing.

For Industries: 
Banking
Overview

FICO® Centralized Decision Service provides a clear, explainable view of your decision strategies across your enterprise and processors to enable consistent and profitable customer treatments. It applies risk analytics, strategy improvement, automation and other streamlined capabilities to help you make better, faster decisions. Intelligent, easy to use dashboards and KPIs provide business and regulatory insights that facilitate exploring what-if scenarios and impact testing.

FICO Centralized Decision Service reduces the friction on your IT department by empowering business users to update decision rules and other system parameters in real time. Its cloud-based, open platform allows you to rapidly incorporate business or technology changes when you need them, so your decision systems are always aligned. Ultimately, it arms your experts with the tools they need to apply real-time insights to specific business challenges, so you can always stay ahead of the competition.

For Industries: 
Banking
Overview

Client: Home Credit Group, an international consumer finance provider with operations in 11 countries

Challenge: Develop a more agile approach to decision-making to enable radical but sustainable growth.

Solution: FICO® Decision Management Suite

Results: In addition to improved operational performance, reduced costs and increased profits, Home Credit Group has created a centralized decision engine that accelerates its ability to branch out into high growth markets quickly in order to massively diversify its scope of business.

For Industries: 
Technology, Banking
Overview

Every year, Vantiv processes more than 17 billion payment transactions for nearly a half-million retail merchants in the United States. Because there is a contingent risk involved in taking on a new merchant, Vantiv sought to ensure that it minimizes that risk and makes appropriate pricing offers. Specifically, they needed a way to speed up its merchant onboarding process, which was being done manually and could take up to nine days. They chose FICO Merchant Acquiring Solutions, powered by FICO® Decision Management Suite. As a result, Vantiv has cut merchant onboarding from days to minutes, which helps facilitate the company’s rapid growth and opens up new possibilities for partnerships with independent sales organizations and merchant banks.

For Industries: 
Banking
Overview

One of the key aims of PSD2 is tackling fraud — particularly fraud related to remote payments; for example, those made online. Card-not-present fraud has plagued the industry since the introduction of chip and PIN caused fraudsters to switch their attention from in-store to the growing online channel. The inability to prevent much of this fraud has motivated the European Commission to try to tackle it with PSD2, the revised Payment Services Directive. Their weapon of choice is Strong Customer Authentication.

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.

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

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 

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