Lenders are realizing that although traditional models of managing their credit risk are important, they may not always be enough in scaling their lending businesses to cover new customer categories. Lenders are now looking at more advanced and innovative approaches to manage risk.
Data analytics and categorization technologies offer opportunities for lenders to effectively monitor and minimize exposure to credit risk. These technologies enable lenders to capitalize on data they already have or can capture quickly and efficiently to evaluate borrower risk. Datasets like spending patterns, payments delayed and defaulted, to mention a few, are all dynamic parameters that can help predict a consumer’s financial trajectory and provide a 360° view of their financial behaviour.
With advanced data analytics that can categorize customer financial transactions, lenders can extend credit beyond their traditional markets, as they are able to deploy risk models to assess the creditworthiness of a wider market. Consumers also get the option to maximize the credit opportunities offered by financial institutions. From the customer’s point of view, the credit approval process becomes more seamless, with reduced turnaround time, which results in superior customer experience. Truly win-win.
The power of big data technology and analytics allows lenders to constantly evaluate their customers’ performance and enables them to reduce exposure to risk and creates cross-sell/upsell opportunities to stay ahead of the competition. Lenders can map the financial profile of their customers using traditional sources like spending and payment patterns or non-traditional data sources such as activity on social media platforms, and branch or call centre interactions. Instead of relying on disparate and siloed sources, all the information required is processed enabling a richer decision context.
Risk Analytics in Financial Services – Use Cases & Benefits
Lenders that are fully exploiting these shifts are experiencing a “golden age” of risk analytics, capturing benefits in the accuracy and reach of their credit-risk models and in entirely new business models. They are seeing radical improvement in their credit-risk models, resulting in higher profitability. Laid out below are some of the values that analytics can bring to these business models.
1. Credit Risk: Underwriting. Make better underwriting decisions by using deep learning algorithms to process vast amounts of data and more accurately quantify the risk of credit default. By automating the personalisation of credit risk management, credit pricing can also be flexible enough to build a scalable retail lending business.
2. Operations Risk: Payment fraud detection. Identify and review high-risk payments by using powerful machine learning algorithms that pinpoint the highest risk transactions.
Analytically enhanced credit models can improve lenders’ returns in four ways:
- Greater productivity owing to reduced sales and operating costs.
Targeted and effective origination process like risk pre-screening
- Higher revenues as higher interest income are generated from loan business.
Lenders can increase margin and loan volume by gradually introducing risk-differentiated offers and cross-selling loan products.
- Scalable Risk management owing to the reduction of relative risk costs.
Better selection of risks, with combined risk scores and risk-clustering of customers – Improved monitoring and early warning across credit categories
- Improved capital efficiency
Better calibration and re-definition of the models, leading to reduced risk-weight.
Lenders can seek to collaborate with fin-techs that have capabilities in developing innovative risk models. We are happy to speak to you on how we can use our proprietary technology engine to build new risk models that can deliver enterprise value. Please feel free to speak to us.
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