Predictive Analytics Application for Credit Scoring, Risks, and Compliance Management

Predictive Analytics is a tool that analyses current and historical facts and make predictions about future events. Predictive analytics application uses several techniques, such as data mining, statistical algorithms, predictive modelling, machine learning, artificial intelligence and others to predict future outcomes and insights.

Unlike traditional business intelligence and analytics tool which focus on giving information, predictive analytics provides actionable insight for informed decision making. Predictive analytics are increasingly adopted by many organizations including manufacturing, banking and financial services, energy and utility, healthcare, retail, IT and telecommunication, and such others to analyse the on-going trends and formulate future strategies

Quadrant Knowledge Solutions are working on a series of strategic insights and market research outlook on Predictive Analytics application in various industries. This article explains how predictive analytics is helping banking and financial industries in credit scoring, risks and compliance management.

Learn more from Quadrant’s Predictive Analytics Market Research.

Predictive Analytics Application for Credit Scoring

Predictive analytics applications are increasingly finding its acceptance in credit scoring by banking, insurance, and financial services industries. Credit scoring is the automated rule based decision making process to access buyers’ creditworthiness against purchase of credit cards, unsecured loans, line of credit, insurance policies, residential mortgages, and several others. Credit scoring methods are widely used by financial services industry for managing customer’s credit life-cycle management.  Predictive analytics application for financial service industry, quadrant knowledge solutions

Traditionally, statistical methods are used for credit scoring and scorecards by financial services and credit lending organizations. These methods are time consuming and costly in developing and maintaining an effective scoring system. These methods also lack the capability in terms of individual level analysis for differentiated lending decisions with high degree of precision and accuracy. Emergence of advance data mining and predictive analytics is helping financial service industries in many ways. It helps developing a robust credit scoring system enabling more differentiated decision making for individual credit decisions and portfolio optimization. Application of predictive analytics is viewed as a strategic tool for improving profitability and revenue growth with improved customer acquisition and retention strategies. Quadrant Knowledge Solutions believes that insights from predictive analytics application provide competitive advantage by maximizing revenue growth and minimizing risks.

Governance, Risks, and Compliance Management: Predictive Analytics Application

Predictive analytics are increasingly being adopted to improve governance, risks, and regulatory compliance management. Financial service organizations are required to practice increased level of transparency and accountability for their reliance on automated credit-scoring based decision making. Unlike traditional statistical methods, predictive analytics helps in building robust and accurate risk-models that ensures compliance of ever increasing regulations.  Predictive analytics enables real-time view of risks involved by different products, geography, and other segments. Predictive analytics helps organizations in preventing the most sophisticated frauds and minimizing the level of false positives.

Advance data mining and predictive analytics can perform a number of risk functions, such as stress testing, internal audit, market and credit risks, operational risks, and others. The growing regulatory requirements including the Basel III (third Basel Accords), SCAP, FATCA, Dodd Frank and other regulatory reforms are also driving the adoption of predictive analytics for banking and financial service organizations

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Keywords:  predictive analytics, predictive modelling, risk models, financial service, credit scoring, scorecards, fraud detection, business intelligence, data mining, machine learning, artificial intelligence, basel accords, regulatory compliance, risk management, stress testing, credit risks

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