Credit risk management

One of the vital challenges for banks has been credit risk, as multiple factors go into forming an individual’s risk profile. The process is even more complicated for business borrowers as data across various parameters and periods need aggregation and analysis to create a holistic picture of risk.

Banks must adopt more innovative credit assessment models that can parse vast volumes of data in a short time and dynamically alter risk profiles considering real-time data. Artificial Intelligence (AI) and Machine Learning (ML) play a significant role in processing this data. The number of firms using AI more than doubled in 2017-18, and 40% of financial services organizations apply it to risk. AI & ML combined with location intelligence (LI) adds authentic value across the credit value chain that includes the initial underwriting process, risk measurement and analysis, and more. By leveraging these technologies, BFSI companies offer a significant leg-up over traditional statistical models.

Challenges addressed by credit risk management software

Benefits of credit risk management

Central system feeding delinquency data to the sales engine

Uncover patterns and risks not visible through traditional methods

Insights into collections, delinquencies for making business decisions

Adds a powerful dimension to fraud management

Crucial in risk mitigation and analyzing the risk concentration

Looking to transform your credit risk management?

Features of credit risk management software

Prediction and forecasting of potentially risky

Understand credit profile, repayment history, and delinquency trends by location.

Map-based risk profiling

Identify positive and negative areas in terms of high and low risk for offering credit approval.

Area definition and analysis

Speed up approvals and rejections by considering existing delinquency levels, business establishments in close areas etc. to pinpoint high risk (negative) areas.

Make informative decision

AI-based recommendations help business leaders make quick informative decisions about lending.

Resource planning

Resource planning forecast based on the old data and machine learning (ML) based engines.

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