The Power of Predictive Analytics in Banking

Predictive Analytics in Banking
In today’s fiercely aggressive world where the customer is king, the number one challenge for banks is twofold – adding new customers and retaining the existing ones. With growing consumerism, the common man now has the power to choose. Hence, banks have to play it neatly & swiftly if they have to survive. But of course, this does not mean they have to compromise the basic premise of screening customers before they hand over the money. It does mean that they have to do this real quick and with enhanced accuracy. So how is this possible? Well, predictive analytics is the name of the game.

Predictive analytics in banking is the practice of extracting information from existing data in order to determine patterns and predict future outcomes and trends. It forecasts what might happen in the future with an acceptable level of reliability, and includes what-if scenarios and risk assessment. It is a technology that helps banks & lenders fetch the relevant data of customers, identify fraudulent activities, screen applications, capture relationships between predicted and explanatory variables from past happenings, and use it to predict future outcomes. Here are some of the ways predictive analytics is transforming the core of banking:

Fraud Detection

Frauds are a growing concern for the banking industry, costing them thousands of dollars every year. It is even more dangerous to those individuals who directly lose money. To a great extent, identity fraud can be attributed to the camouflage provided by the internet to the cybercriminals. To combat a cyberattack, banks need to be equipped with similar or rather better weapons such as predictive analytics, artificial intelligence, machine learning, etc. Analytics help determine frauds in advance by inferring near accurate conclusions from processing the provided data. With data integration, utilizing unstructured data and machine learning techniques like supervised and unsupervised learning, we can smell the dead rat even before it dies!

  1. Application screening

    Application screening and portfolio review cannot just be a time-consuming process, it may also mean several customers lost to a simple mathematical algorithm. Predictive analytics can overcome this shortcoming by several notches and process huge volumes of applications without excluding important variables, without delays or errors, without growing tired- all of it with regularity and accuracy.
  2. Cross-Selling

    The best cross-selling is possible through a thorough analysis of customer buying behavior – their likes, needs, and demands. Predictive analytics helps to invest in pitching the right product to the right customer as opposed to posting them with random products. With predictive analytics, banks can rapidly segregate various customer segments and replace them with highly relevant, individualized messages tailored to each customer’s profile, resulting in a higher response rate.
  3. Customer retention

    Every customer needs to feel important and appreciated and the loyal ones even more so. They need to be rewarded periodically. Sometimes it gets too late to retain a customer owing to a large customer base. Predictive analysis helps identify which customers are willing to switch to any other bank and the reason behind their decision. 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
  4. Develop Credit Scoring Models

    Credit scoring models allow optimization of any financial institution process. CRIF provides a full portfolio of modeling tools and expertise such as credit bureau scoring, credit risk scoring, and collection scoring models that empower business analysts, from beginners to advanced modelers, to develop, build, test, deploy and manage predictive models.

CRIF’s predictive analytics tools and software generate hundreds of millions of credit bureau score calculations and risk decisions every year around the world. CRIF’s Cloud-based Origination-Solution-as-a-Service – Sprint, which over 300 institutions use every day for credit risk evaluation, credit origination, and debt collection. CRIF is one of the RBI authorized credit bureau in India. You can also check your online credit score on the website. For more information on the prospects of predictive analytics for your business, Contact CRIF High Mark.

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