“Buy now, pay later” is a tempting offer made by many firms to their customers to increase their customer base. However, both parties need to be aware of the risks when making such credit decisions.

The consumer lending business is centered on the notion of managing the risk of borrower default. Inconsistent or unreliable approaches to credit analysis expose firms to unnecessary risks. Lenders must be able to assess the risk of default for each customer so that they can decide to whom the offer should be granted, or actions should be taken.

Unfortunately, accurately estimating the credit risk of a borrower is the most challenging task many lenders face. Data and analytics can play a huge role in reducing inefficiency and streamlining business operations.

Here, Predictive analytics helps lenders to identify the chances of uncertainty and provide guidance for the identification, measurement, monitoring risk, and better credit decisions.

Predictive analytics (Predictive modelling) is a subset of business intelligence that analyzes past behaviors to predict the future. It is the method of analyzing and modelling data to gather insights that can be used to make meaningful decisions. It includes mathematical techniques, machine learning techniques, and processes that are applied to historical data, identifies trends, and make the best valuation of what will happen in the future.

Predictive Analytics provide Data Insight

Many a time, borrowers who might seem to make for perfect candidates for loan origination might show erratic payment and financial behavior, once their loan is approved. This is something that the underwriters might not be able to predict at the time of loan origination. Delinquency prediction helps the lenders see the risk by observing and studying a large set of consumers and their financial behaviors using statistical models that help in removing biases and errors to give you a score, close to perfection.

The power of big data technologies and analytics allows firms 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. The level of default/delinquency risk can be best predicted with predictive modeling using machine learning tools. Predictive models combine vast amounts of data and sophisticated analytic techniques to make predictions about the future. They help us reduce uncertainty and make better decisions.

Typically, the workflow of a predictive analytics application follows these basic steps:

1. Import data from varied sources, such as web archives, databases, and spreadsheets.

2. Clean the data by removing outliers and combining data sources.

3. Develop an accurate predictive model based on the aggregated data using statistics, curve fitting tools, or machine learning.

4. Integrate the model into a load forecasting system in a production environment.

To get maximum benefits from Predictive Analytics solution, businesses need to follow a few steps, which include:

1.Ensure that company-wide data policies are aligned towards making the data easily accessible, as well as establishing a pipeline to continue a streamlined data collection process.

2.The integration of predictive analytics platforms would also require financial domain experts to work in collaboration with data scientists to arrive at more accurate models

Without a shadow of doubt, predictive analytics can take things a level ahead by providing valuable insights to decision-makers for designing the next action.

Insight Consultants Offer

Insight Consultants provide comprehensive business insight into credit risk-management of lenders. The solution uses sophisticated credit scoring models to allow credit risk managers and credit analysts to create predictive scorecards. It also incorporates defined metrics that provide a unified view of customers across lines of businesses and channels. The solution focuses on the three key tenets of efficient risk management in lending: Informed Decisioning, Enhanced Portfolio Management, and Fraud Prevention.

If you’re ready to take the next step Contact Us  and find out more about how Predictive Analytics can transform your credit decisions!