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Monday, 4 November 2013 00:05 - - {{hitsCtrl.values.hits}}
Having a database of over 3.9 million individuals, CRIB acknowledges that the real value of datadepends on the quality collected and how it can be used to enable lending that does not turn into bad debts.
With the intention of educating credit officers on how value addition can be achieved through credit information and to bring the professionals up-to-date with the global trends in that arena, CRIB held its third annual symposium last week on the theme ‘The Changing Face of Credit Information: A World of Value Addition’.
The event, at which Central Bank of Sri Lanka Deputy Governor/CRIB Chairman Ananda Silva was Chief Guest, featured Compuscan Credit Bureau, Uganda, Managing Director Mike Malon as the speaker to facilitate the technical session.
The technical session was facilitated with its focus on infrastructure that supports the industry and Malon highlighted areas such as the intelligence required to aid CRIB in offering better services, the type of data needed to assist credit officers, and ways of targeting new data sources.
Importance of credit data
With reference to Compuscan Credit Bureau, Malon shared that the institution understands the importance of accurate and reliable data to support business functions and decision making. Striving to equip its clients with the most comprehensive and accurate data, along with being on the lookout for opportunities to expand data offering, Compuscan utilises its consumer and business data toallow businesses to make better informed decisions, and reduce risks throughout the credit life-cycle.
Compuscan is a group of companies formulated under a holding structure that has three organisations under it. Having the credit bureau, credit training academy and the credit software solutions under one umbrella, the analytics area of the company focuses on credit scores and consumer details. While these are typical functions of any credit bureau, Compuscan adds value by using the data to ‘predict’ the outcome.
Although operating a credit bureau is impossible without wanting an ID card for client verification, Compuscan builds identification systems to run successful credit systems and help facilitate the entire credit program processing.
Sharing on what is done with that information, Malon said: “We take that information and build models for solutions within the market. This we do to help our clients with decision making. We allow them to analyse how accounts that are classified normally as risk accounts are performing in other institutions. We make the institutions realise they are doing with such customers. Are they going to let it be, or grant more credit to them? We look at the customer across the lifecycle and believe that any credit bureau should be able to do the same.”
In the marketing component of the credit bureau relationship, there is information when identifying customers. While there is a need to pre-screen information, a credit bureau should be able to assist institutions by working with marketing service information.
“If there is a decision to be made on a branch opening, the borrowing profile of the customer should be known. Although this information lies in the system of the credit bureau, the question is whether the credit bureau is able to deliver that information when it is needed the most,”he observed.
Use of credit data
The traditional way of using credit information is at the time of engaging with the new customer. However, there is more that can be done with this information, according to Malon.
“An institution should think of how they can automate the entire application process. Can credit scoring be done? What is in the credit scoring? 
Is it verifying information? These questions should be addressed,” he emphasised.
With the customer relationship managing component being the element that monitors the portfolio and identifies the activities of the customer in the lifecycle, Malon pointed out there is the behavioural, application and collection cycle.
“Even though an intelligent query will lead to accessing credit bureau data, it is not about that. It is about how the data can be used to make predictions of the customer’s behaviour,” he asserted.
There being credit discussions on technical information symmetry, this indicates the borrower knows much more than the banks. Observing a widening gap between the two, financial institutions should explore ways of getting their hands on additional data, he said.
As the purpose of having improved credit information is to allow improved decision making, it is imperative the collected information be accurate and relevant. The usefulness of bureau data can be measured in two ways, one is by data length whereas the other is by the amount of data an institution already has.
According to Malon, over a period of time what typically happens is that banks and credit bureaus become quite “creative” in terms of providing information, but become relaxed and forget the new data. “There is limited innovation in terms of new information. There should be new ways of getting about this and the board should encourage that they have the new data.So why wait to share the information? The more data the better and for this the banks should be pushing innovation,” noted Malon.
Missed data: Reference to gold pawning
Gold pawning is a highly popular product in the Asian continent, but in regions such as South Africa, it is almost a backstreet transaction. While this data is certainly meaningful, the fact that this information is not shared with the bureau is noted to be a “big loss”.
“That fact that gold pawning is secured makes absolutely no difference. It is like giving a mortgage and not capturing that data because it is secured. This is such hard core data of how people are paying, irrespective of the price of gold. This is real data and so much can be done with that information,” asserted Malon. Stressing it is important to look at intelligence when looking at data, he opined there is modelling and predictive data when it comes to gold pawning thatSri Lanka is not even getting close to capturing.
By using credit data, there are assumptions that in credit risk modelling there is sufficient amount of past performance that can be modelled to predict the future performance.
If information of individuals using gold pawning is captured, by being able to identify those not making payments, the likelihood of such clients not making payments for future transactions could be predicted.
“A bank can measure the credit risk and associate it with the transaction together with the gold value. People who pay debt are more likely to pay debt no matter what the situation simply because they pride themself with the fact that they want to look after their credit reputation with an intention to pay.”
Credit scoring
The core fundamental in which a credit scoring is built is that the future fundamental and past behaviour of the customer can be used to predict the future behaviour of the individual. While principally anything can be modelled, and any pattern can be predicted, the profit maximising objective has to be set.
Malon noted that for profit maximising, a small extra reach will help the institution. Credit scoring, along with advanced decision making tools are available to help institution to reach credit heights whereas advanced solution can help institutions reach out to best customers with the least amount of error.
As credit score is typically a statistical modelling technique predicting the repayment of the credit facility, there are three different types which are, application scoring, credit lifecycle scoring, and the credit bureau scoring.
“A scorecard is just a tool, a risk assessment tool that adds up some numbers. The characteristic of it is typically the questions in the loan application form, and the attributes of it is the answer to those questions. The weigh is the points given to the attributes, and the scorecard is the sum of weights,” explained Malon.
Scoring takes into account the performance and the observation period. Typically, details of an entire group of applications are considered. While some applications would have been rejected initially, the same applicants have later turned out to be fantastic customers.
“The use of the scoring system is to analyse the good and bad accounts, and how those can be turned into simple numbers.The objective of the scoring exercise is to minimise the bad and exercise the good,” he added.
The concept of odds
In statistical odds, the numbers of good and bad account are much talked about. Breakeven odds are where the profit of the goods is equal to the loss of the debts, and looks at how many good accounts are needed to cover the bad accounts.
As an organisation in credit, it is imperative for all institutions to know its breakeven odds so it can work out with the marketing function what products should be focused on to cover the debts. Information increases or decreases the likelihood of odds and when looking at the scorecard, the individual components of the credit bureau must be considered and should be converted into useful information. The aim is to get the highest amount of odds to build a score. “All of these concepts are becoming an important component to analyse past data and to look at predictive elements. This allows using value to predict the future occurrence of likely events,” Malon said.
“The scorecard tells you want to do. It is easy to turn down applications that seem negative, but when you get to the middle grey areas applications, it gets a little tricky. The scorecard tries to sort out the difficult to evaluate applications. It sets a likelihood that the account will be paid,” he elaborated to participants.
Policies for loans
Depending on where a customer falls on an odds distribution, according to Malon, an institution might want to set policies when issuing loans. “You can’t stop lending. The success of the scorecard lies in the ability to separate the accounts. Users need to determine the risks and need to link this with the bank’s risk appetite.But at the end of the day, there is only so much separation that will affect the present way of making decisions.”
The purpose of a scorecard is to push define clearly the good and bad accounts. The scorecard needs to be tuned to the banks and should be in line with the portfolio of the credit bureau. The strategy should be simple, that is to reduce bad debt as much as possible. For this some institutions might have a cut off mark and consider only those who have scored above the minimum required points.
The strategy could also be that the financial instructions would deal with only the top best customers. The ones below, they are not interested. “That is a compromise. Some banks will want to find a balance between maximising good accounts, reducing bad accounts and managing the in-between, because they acknowledge that such a space exists. If the in-between is acknowledged, setting limits to the customer should be exercised.
Right use of credit rules
With bureau scoring rules being very clear, credit officers can allow easy-to-follow policy instructions. For this he stressed there is a need for a limited training investment to educate the staff.
The flip side of credit scoring is that the model is built in such a way that one perceived that loans can be extended but only taking into account the score. If a negative element is noted in the application although the score is qualifying, the request for the loan should be rejected. “There is a time where people experience a gut feel and say that ‘I don’t trust that customer’. You may have your own business rules, but how you should get about that should be carefully thought through,” Malon told credit officers.
Pix by Sameera Wijesinghe
