An unsecured loan is a loan issued and supported solely by the borrower’s creditworthiness, rather than by any kind of collateral. The terms of such loans, including approval and receipt, are therefore most often contingent on the borrower’s credit score. The consumer lending business is centered on the notion of managing the risk of borrower default. Credit scoring systems and predictive analytics model attempt to quantify uncertainty and provide guidance for identifying, measuring and monitoring risk.
Benefits of Predictive Analytics in Unsecured Consumer Loan Industry
Predictive Analytics is a toolbox that includes mathematical techniques and processes that are applied to historical data to study correlations, identify trends and predict possible outcomes by quantifying the uncertainty and the characteristics of the variation.
Predictive analytics continues to gain popularity, and research proves that there is a gradual move toward credit scoring strategies developed using data mining and predictive analytics. Predictive models can benefit lending companies in the following ways:
- More accurate estimate of a borrower’s default risk,
- Reduced operational cost,
- Reduced operational risk,
- Easier consumer lending decisions,
- Reduced labor hours,
- Develop credit score strategies,
- Improve overall quality of borrower portfolio,
- Reduced bias in the loan process
Predictive Analytics enhances the Lending Process
In this competitive world, growing customer base and keeping them engaged is considered the most challenging task for a lending institution. Important issues like ensuring customer loyalty, retaining and attracting different types of customers or cross-selling products suited to them, fraud detection, application screening, have been areas of concern in the unsecured consumer lending business. Predictive analytics has played a pivotal role in streamlining the lending process. It helps lending companies gather relevant data of customers, identify frauds early, application screening, study past data and identify trends and patterns from past happenings and use it to predict future outcomes.
Predictive analytics, Machine learning, Big data, Data mining and Stream computing are some of the tools that help lenders in identifying frauds which helps companies and institutions to take steps to prevent such frauds. Analytics can be used to recognize frauds that are not very obvious and then predictive analytics can be implemented on them to analyze them further.
Predictive analysis can help lenders process large volumes of applications, without excluding important variables, without delays, avoid errors with regularity and steadiness. Also, it can help lenders expand their customer base by acquiring the right type of customers.
As much as acquisition of customers is important, retaining them is no piece of cake. Predictive analytics can help lending institutions retain customers by keeping the right customers longer, identifying customers that are most likely to defect. It helps recognize churn patterns and develop profiles of users who have left, to get an insight of why they left and formulate strategies to keep them engaged in future.
Predictive analysis helps identify which customers are willing to switch to other lenders and the reasons for this. It examines customer’s service performance, spending, past service and other behavior patterns to predict the likelihood of a customer wanting to stop its services anytime in the near future. With predictive analytics, lenders can segregate various customer segments and replace it with highly relevant, tailor-made messages to each customer’s profile, resulting in a higher and faster response rate. This ultimately helps deliver the right product to the right customer.
Securing one profitable customer can be so challenging for lending companies, therefore cross-selling another product to an existing customer can be of great help. Predictive analytics helps examine customers’ usage, spending pattern, and other behavior and lead to effective cross-selling of the right product at the right time. Predictive analytics helps lenders identify which customers should be the focus of new customer engagement efforts, previous factors that enhanced returns on customer engagements in the past and use that information to understand why customers responded to certain messages.
Predictive analytics allows lending institutions to keep up their relationship with the customers by giving them the right products suited to their needs and matching individual preferences seamlessly. As a lender, talking about labor costs and how they are affecting the bottom line might be the bane of a company’s existence. Predicting the number of labor hours required is based on past data of customers and patterns in their behavior. This can bring down the labor costs for a lending company.
Where BizAcuity comes in?
BizAcuity has assisted clients in this industry address several concerns:
- Budget prediction, that is, predict how much investment by the client is required.
- Predict how many loans the client can sell by studying customer behavior.
- Predict labor hours in a month thus reduce manpower costs for the company.
- Analyze past data of existing customers and suggest client’s employees to reach out to such customers.
Digitally powered decision making helps Unsecured Loan companies and other lending institutions in rapid customer acquisition and reduce operational costs. As a result, lending companies grow fast and reap attractive returns.