Credit Score Cards

 

Information Technology as an industry has grown up in leaps and bounds. You may not find any organization on the planet which does not have any IT involved.  This has given rise to a lot of jobs supporting the IT functions. Salaries have increased tremendously in IT compared to other business areas. The overall economy had gone up which increased the tendency of people to afford & buy more & more.

This has increased the usage of Credit in everyday life. “Buy now pay later” syndrome became common. Everyone started using the credit cards and also started availing credit or loans for big purchases like home, car etc.

Eventually, this resulted in many people avoiding or defaulting on the payments. This is where applying analytics for assessment of the risk of providing the credit came along and the birth of credit scoring.

Credit Risk is the risk of losing a bank or credit giving company will incur when Customer does not repay the mortgage, unsecured personal loan, auto loan, credit card amount, overdraft etc.

In the early days of lending businesses used to judge borrowers based on 5 Cs:

• The character of the applicant
• The capacity of the applicant to borrow
• Capital as backup
• Collateral as security for credit
• Conditions which were mostly external factors

Then Credit Scoring was introduced by Fair Isaac which is now commonly known as FICO score.

Credit Scoring in simple terms giving some numbers to customers based on certain parameters like age, earnings, accommodation type (owned or rented), expense history & payment history etc.

There are 3 types of Scorecards which are currently used:

Application Scorecard:  This is mainly used in scoring the customer's applications for credit. This tries to

predict the probability that the customer would become "bad". The score given to a customer is usually a three or four digit integer which is finally used to approve or reject the credit application of the customer. This is where you get messages from Banks that you have pre-approved loans or Credit cards.

Behavioral Scorecard: This is mainly used to identify or predict which of the existing customers are likely defaults on the payment so alternative measures can be taken to contact the customers & ensure that payments are received on time.

Collection Scorecards – This is mainly used to arrive at how much loss the company will incur, due to nonpayment from groups of Customers.

How businesses are using Credit Scorecards:

• Banks are using them to separate good borrowers from bad borrowers
• Financial institutions are using it to determine credit limits
• Early detection of high-risk account holders to reduce potential losses
• Improved debt collection
• Insurance companies are using it for the cost of insurance product for a Customer

Today applying analytics to the data to get such insights is of prime importance. 

Social Media Analytics

The Internet has taken the world by storm in the last few decades. Some of the surveys show that it has grown over 2000% in the last 10 years. Imagine how many people globally are online at any point in time. Being social animals, we like to talk about our feelings, good or bad, with our family & friends.   

Today people are using internet for communication more, than any other medium, This has given birth to social media sites like Facebook, Twitter, Orkut, myspace, digg & the list will go on. Imagine how much data is collected on these sites in one day.

So companies have started using this huge unstructured text data in these comments, sentiments of people for their product improvements, for Customer service improvements, for understanding what customers are talking about competitors. This is called Social Media Analytics.

In the recent example of Toyota recalls, they are closely watching these social sites to gauge what Customers are talking about their brand, quality & take necessary measures to correct their actions.

But social media analytics isn’t just about damage control, it can provide precise data to help you better understand your customers and discover new business opportunities.

Here are typical steps in implementing social media analytics.

• Collect the huge amount of unstructured data – comments, blogs, call center notes, twits from social sites
• Using statistical analysis & Natural Language processing (NLP) on texts & words to break up the information into good or bad
• Use categorization, classification & association methods for text processing
• Further, identify the categories on which these good or bad sentiments are applicable from the data
• Brand awareness, trust, loyalty
• Customer service
• Product improvements
• Competitor analysis
• Geographic locations
• Produce the results using visualization tools

Here is how various businesses are using Social Media Analytics:

• Manufacturing – using the warranty data combined with Customer complaints to improve products & reduce warranty costs
• Healthcare – find connections amongst the claims received to flag further frauds
• Banking & Finance – use the social network profiles data to improve credit calculations, identify reasons for Customer churn
• Insurance – claims cost prediction& fraud detection
• Telecom – improve the customer experience for new products introductions
• Hospitality & Travel – listen to guests comments to improve repeat customer rates

Social Media Analytics will continue to gain importance in near future !!