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They began to see that the technolo- gy could, in seconds, provide granular insight into prospective borrowers by interrogating line-by-line their finances; that score card changes could be made at the press of a button; that borrower screening did not always need costly credit bureau searches; that decisions could be 100% in line with a lender’s credit policy; and that an electronic audit trail was there to protect them from mis-selling claims. As a result of many of those conver- sations, we have now reached a stage where the technology is familiar to most in this industry. A sizeable number of lenders at all ends of the spectrum - from high-cost short-term credit to mortgage - are utilising it. Using the Rogers’ technology adoption life-cycle, we have moved on from ‘innovators’, through ‘early adopters’, and will shortly reach the stage of ‘early majority’. Those that already have it, have been benefiting from what is better termed assisted decisioning’s ability to deliver optimal lending outcomes (for both the borrower and the lender) and the ease with which it permits lending to be scaled according to appetite. Such lenders are currently looking to take automated underwriting to the next stage: leverage the technology going forward so that it can deliver even greater value. Two areas that are particularly exciting in this context concern machine learn- ing and multi-bureau credit searches. Machine learning (ML) involves the use of algorithms that improve process- es automatically through the mining of data. Essentially, it amounts to the formulation of best practice in an algo- rithm that, over time, gets better and better at doing its job.
Up to now the only way companies had access to this sort of sophistication was to pay a data analyst to go away with their raw data and, over a period of many weeks, work out what lessons could be learnt. The result was that not nearly enough data analysis was conducted by a typical lender, which is incredibly ironic given that lenders are ultimately data-driven enterprises. Platforms such as Auto Decision Platform (ADP) by LendingMetrics, however, can empower lenders to harness their data for immeasura- bly better quality decision making. A lender can set an ML process in motion - swiftly deployed using ADP - that will tell them in minutes what, for example, are the best combination of predictive values on which to base a loan decision. And there is no costly data analyst to employ. Lenders have always aspired to be able to use multi-bureau credit checks in their decisioning. Individual bureaus tend to have their strong and weak points, so a multi-bureau search makes a lot of sense. However, the majority of lenders are limited to a single search from one of the three usual providers. Given the extra cost, two credit search- es are rare, three even rarer, and only if the loan is of a size that warrants it. In this instance, platforms such as The LendingMetrics Exchange (LMX) are, for the first time, making multi-bureau searches possible and affordable. At long last, on the basis of one contract, lenders can tap into two bureaus (in the case of LMX this is Equifax and Experi- an) to deliver a better informed search. Combined with ADP, this power couple allows lenders to easily apply and adjust their multi-bureau strategies at will. While these two facets of assisted decisioning are being enthusiastically
embraced by the sector, there has been one other that has not had the speedy adoption that I first imagined. Open Banking has not really gained the trac- tion that was first originally thought for a number of reasons. While it does deliver real-time transac- tion access, unlike bureau searches, it does not showmissed loan instalments or previous adverse history. It also intro- duces several extra pages of friction to consumers, which not all of whom will push through. Furthermore, unlike Open Banking, credit searches always deliver a 100% response. The only way I see this situation chang- ing will be if the use of OB data is com- pelled by regulators, as it is in countries such as Australia. And there is no indi- cation of this being on the cards in the UK just yet. Looking ahead to the next six or seven years of automated decisioning, I am certain that what we have seen so far is just a small sample of what is to come. The future for lending is going to be more and more about data and the technology that leverages it. As I keep on saying to lenders, their USP is not simply their products but also their data. Those that fully appreciate this and act accordingly are going to the ones that succeed.
Above: LendingMetrics Managing Director and Chief Technology Officer Neil Williams
www.lendingmetrics.com
Metrics Monthly | 09
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