Predictive analytics software deployment: 4 pitfalls to watch out for

I have posted an article on SearchBusinessIntelligence.in about the 4 early signs to watch out while catching up on analytics initiatives.

you can read it at

http://searchbusinessintelligence.techtarget.in/tip/Predictive-analytics-software-deployment-4-pitfalls-to-watch-out-for

Fraud Detection & Prevention

 

The financial industry is facing the fiercest competition in current time after the economic meltdown. Banks are using all avenues to grow their customer base considering the survival aspect. This has lead to tremendous volume growth in banking accounts applications, credit card applications, and financial transactions.

Obviously, as a consequence, the number of fraudulent applications and transactions is also rapidly growing.

With Digital Transformation new payment channels like prepaid cards, e-payments & now mobile-payments, fresh opportunities for frauds are emerging.

Some of the industry research shows that:

• Credit card frauds losses are over 8 billion USD per year
• Insurance policyholders have to pay a higher premium up to 5%
• Total fraud Losses are estimated at over 30 billion USD per year

Frauds can be classified into various categories as below:

• Credit/Debit/Charge card fraud
• Check fraud
• Internet transaction/wire transfer fraud -
• Insurance or healthcare or warranty claim fraud – overpayments, false claims
• Subscription fraud – use of telecom services with false credentials
• Money laundering
• Identity theft or account takeover

Analytics approaches to detect & prevent Frauds:

• Combine historical fraud data with industry knowledge & external market data
• Create a proof of concept to test the history data to determine fraud cases
• If historical data is not available then anomaly detection or outlier detection is used
• Apply the statistical model for fraud detection
• Models are based on past spending patterns, demographic information
• Further text mining & link analysis for probable associations to find deeper frauds

Benefits:

• Increased number of identification of fraud cases
• Dollar savings from fraud prevention adds to the bottom line
• Protect the customer base from financial loss or identity theft
• Improvement in service helps to differentiate in the highly competitive market

How companies are using it:

• Financial institutions using it to identify frauds in leasing contracts
• Banks are using it to detect credit card, wire transfers, check frauds
• Insurers are using it to detect fraudulent claims to save the losses
• Healthcare provider can optimize the medical loss ratio by detecting claims frauds 

Today with help of Big Data platforms, companies can store all the historical data they have which can help in better fraud detection.

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.