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Digging out shady salespeople with data analytics

Innovative analysis of transaction data can help customers of financial services

Innovative analysis of transaction data can help customers of financial services

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One of the good things that have happened with social media is that instances of sharp overselling in financial services find much wider publicity. Someone reads of being mis-sold an insurance policy or a mutual fund and there is a flood of me-too replies which makes everyone realise that this is actually a widespread practice. As always, many, perhaps most cases are those where there was no overt violation of rules. Instead, the financial products that were sold would be utterly unsuitable in the opinion of any financial advisor who has the client's interests in mind. Or, products were sold on the basis of oral promises and assurances which could later be denied easily.

Very few of these cases become actionable complaints and even fewer eventually get resolved satisfactorily. What's worse is that the few that do require extraordinary levels of persistence from the wronged customer and support from someone who knows what they are doing. Clearly, the wrongdoers rely on the fact that only a vanishingly small number of customers will actually carry through with their complaints.

However, looking at such cases, it is clear that a vast majority follow a pattern. There are a handful of techniques that are followed with almost every customer. In the case of mutual funds, before SEBI abolished entry loads, it was 'churning' - endless buying and selling of fund holdings to generate high commissions. In the case of ULIPs, agents would pitch policies saying that the customer could redeem after three years - which would maximise the agents' commissions and minimise the customers' returns.

The detection and resolution of these practices are still stuck in what one could call the police complaint paradigm. When a customer figures out that he has been hard done by, he goes and complains to SEBI or IRDA. Then there might be some sort of inquiry, and if there is actual provable evidence of some wrong having been committed, then some kind of action might be taken. This is essentially the system of police complaint, inquiry and court case transposed into a realm where it is only marginally useful.

While this system of complaints and inquiries can go on, financial regulators could look at using analysis of large amounts of actual transaction data (big data analytics) to enforce transparency and warn potential customers of potential problems. Here is a plausible example. How about using the entire mass of transaction data to make it publicly visible what is churning and who does it. For example, suppose we, the investing public, could go to a website and find out for each seller, what the ratio of the returns earned by investors and the commissions earned by the seller was. Along with aggregate data on what the range and distribution of this data for the entire universe of financial intermediaries were. So I would know that, on an average, Bank A makes Rs 12 commission for every 100 rupees of returns that its customers' investments earn while Bank B earns Rs 36. For insurance, suppose I could see that for the agent who has approached me, some 55 per cent of ULIPs lapsed early, while the average for all agents was 32 per cent.

These are just a couple of examples, but it's quite possible that for every kind of mis-selling that happens, it's possible to have publicly available statistics that give some indication to the customer about the quality of an intermediary. It would also create a benchmark that they would then have to strive to improve. Analysing large amounts of data, and looking for the right clues, can yield surprising benefits. The kind of secrecy that pervades the financial services businesses enables shady practices to be carried out quietly. Enabling customers' access to information is the best way to actually change things for the better.

Also read: The perils of social investing

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