Assessing the loan-worthiness of an applicant is no easy task. Credit scores, a three digit metric that offers lenders a way to benchmark your worthiness against the average applicant, is often seen as a flawed system that punishes the “guilty” with a sentence that is much larger than the “crime”. Regardless of what the common perception of a credit score is, the bottom line is that your credit score matters and has a huge impact on how easily you can get loans and what rates you are charged for it.
Scoring is an evaluation method especially for the purpose of credit rating. Big Data influences the calculation of probabilities by opening up additional data sources (e.g. social media data) and by providing enhanced possibilities of analyzing. As a consequence, scoring is becoming the basis of decisions in more and more areas of life.
Big data scoring redefining credit rating
Over the past few years, a number of new start-ups have cropped up that have redefined the way credit scoring is done. Neo Finance, for example, is a Palo Alto based lender for auto loan borrowers. Instead of using the conventional scores to assess the credit worthiness of the borrower, Neo Finance looks at the applicant’s job history and the quality of their connections on LinkedIn to assess their loan worthiness.
While the viability of such social credit scoring mechanisms is to be assessed over the long term, the bigger impact to the industry is being dealt through sophisticated big data assessment systems. Unlike the conventional score that primarily uses an applicant’s transaction history to assess their loan worthiness, these new start-ups make use of a much larger data pool.
Bid Data Scoring in action
ZestFinance makes use of all kinds of data to assess the loan worthiness of a customer. The company is co-founded by Google’s former Chief Information Officer and considers all data about a customer as credit data. The company uses technology that analyzes thousands of variables including factors like the number of times a debtor has moved house, how well they use capitalization on a web form, etc. to build a profile of an applicant that assesses the risk in a much more efficient way than a conventional score does.
At present, most companies owning such alternate credit worthiness measurement systems have their own lending infrastructure. The next decade should witness a greater adoption of such technologies among banks.
How Big Data Help Financial Services Firms Manage Risk and Stay Competitive
Financial services organizations around the world are experiencing drastic change in order to thrive in a market that has changed so dramatically, they need to improve their operational efficiencies, detect fraud faster and more accurately, model and manage their risk, and reduce customer churn. To accomplish this, financial services firms are turning to big data technologies.
- Fraud Detection – Flagging anomalous activities in real time help prevent potential security attacks or fraud. Analyzing incoming transactions against individual and aggregate purchasing histories and take appropriate action if the activity falls outside the confidence level of normal behavior.
- Customer Segmentation Analysis – Banks can create a more meaningful and effective context for marketing to customers if they can define distinct categories, or “segments” in which each customer belongs and plan sales, promotion and marketing campaigns accordingly.
- Customer Sentiment Analysis – Big data can analyze comments on social media or product review sites, enabling them to quickly respond to negative or positive comments. Banks can effectively connect with their customers and gain a better understanding of the types of banking products and services that customers find valuable.
- Risk Aggregation – Big data techniques can be used to gather and process risk data in order to 1) satisfy risk reporting requirements, 2) measure financial performance against risk tolerance, and 3) slice and dice financial reports.
- Counterparty Risk Analytics – Whenever a firm engages in a business transaction with another party, the risk of doing business with that party must be priced into the terms of the deal. Big data techniques provides the performance, scalability, reliability, and the easy access and delivery of data to drive the key components of a counterparty risk analytics system.
- New Products and Services for Consumer Credit Card Holders – Making new products and services available to consumer card holders is an ongoing initiative for banks. Data Platform can be used to provide new products and services for consumers in real time at a leading credit card company.
- Credit Risk Assessment – Big data enables banks to pull in customer data on everything from deposit information to customer service emails to credit card purchase history in order to gain a holistic view of their customers. Financial institutions have the ability to construct an in-depth view of their customers so they can properly provide accurate credit scoring and analysis.
The future of credit rating will no more be the same. If you are keen to learn more about Big Data Scoring potentials, Anglo African team will be pleased to provide you further details, contact us on 2331636 or by e-mail at firstname.lastname@example.org