“Here, you see, it takes all the running you can do, to keep in the same place. If you want to get somewhere else, you must run at least twice as fast as that!”
Thus says the Red Queen in Alice in Wonderland. This is very true of the modern business world. We live in times when business advantages are short-lived. Analyzing historical data to plan tomorrows is a kind of sluggish way of doing business. That is why predictive analytics is important.
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Predictive analytics helps to:
- Earn low-risk customers
- Know about developments impeding repayments
- Reduce service cost and increase profit
- Provide more individually customized services
- Run better-targeted marketing campaigns
- Identify risk events affecting borrowers
- Improve the maturity of your very analytics
These will lead your business to become more agile, competent, and profitable.
You acquire some customers. Some of them end up unable to pay back, some turn out frauds, some repay only because of good market conditions, and the rest repay as per the agreement. You analyze this data and assess your overall risk profile, and based on it, you make your future decisions.
Now, what about the damage already suffered?
What if your analyses and risk profiling were more accurate before acquiring customers and through their repayment period? That is what predictive analytics is all about Predictive analytics software has become an inevitable tool for enterprise risk management in many industries, including banking, insurance, mortgage, healthcare, medicine, travel, and retail. Remarkably, in the parlance of analytics, risk has become almost synonymous with credit risk.
The key function performed by a risk-analysis product is transforming uncertainties about the future into probabilities that can be used in business decision-making. Various techniques are used for predictive analytics. Software products rely on multiple techniques, but also on third-party data about customers so that lenders are able to identify risk levels around repayment.
Credit scoring and rules-based decision making are important for risk management in financial service organizations. They need actionable and predictive rules that can bring about continuous business growth. By studying the borrowing, spending, and repayment behavior patterns of applicants (individuals and institutions), they can create scorecards. By forecasting the amount to be recovered, schedules for recovery, cost of collection, and methods of recovery, they can strategize the lending and the inventory. Thus, predictive analytics makes businesses agile and competitive.
