22 March 2018
Paul Thomas

Risk Decisioning and Analytics

Paul Thomas - Provenir

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3 trends to dig into at Money2020

17 October 2016  |  6327 views  |  1

The Money20/20 agenda is always packed; the event inspires insightful discussion, debate and collaboration every year. And this year promises to be no exception. I'm especially looking forward to chewing over these three topical trends in Las Vegas:

1. The Checkout Challenge

It is a retail priority to reduce friction at the checkout – online, on mobile and in store. We know that a high percentage of potential sales fail to convert – in fact, almost three-quarters of digital shopping carts get abandoned. That figure represents a huge opportunity lost and merchants know they must do everything they can to address the range of factors that contribute to it. Which includes replacing clunky or inconvenient checkout processes with quick and simple ways to pay. I'm expecting the latest innovations in automated risk analytics that are helping providers return rapid credit decisions for seamless checkout to be discussed in Las Vegas.

2. Levelling the Lending Playing Field for the Underbanked

The plight of consumers and businesses that can't get credit and struggle to gain access to financial services highlights the gap between the banked and the un(der)banked. It is a global problem; PwC cites the World Bank Group's estimation of 2 billion adults – 42 percent of the population – being outside the financial system and goes on to size the unmet deposit demand of the un(der)banked at $360 billion.

When the sticking point is data – having the right type and enough of it to accurately calculate risk - financial technology can help. After all, would-be borrowers generate huge quantities of data online; if this can be tapped into then it can be useful to credit scorers, complementing existing data sources; possibly even providing an alternative route to a credit decision. Customers can be relieved from some of the inconvenience of manually providing information if data can be incorporated into risk processes automatically. In this way, new sources of data and advanced data analytics can help level the lending playing field for the underbanked.

3. Fighting Fraud with Machine Learning

The intelligent use of rich data sets is enabling financial service providers to raise the bar when it comes to meeting customer expectations and minimizing risk. Attracting much attention of late is the potential of artificial intelligence and machine learning. The insight locked inside huge volumes of data can be brought to the surface through machine learning which can spot patterns and learn from its findings to keep predictive models fresh and up-to-date and minimize costly manual intervention. In this way, the predictive capability of machine learning can help improve and speed up fraud identification. We can expect the many and varied applications of machine learning in financial services to generate interest, not only at Money20/20 but for some time to come. 

TagsSecurityRetail banking

Comments: (1)

Ketharaman Swaminathan
Ketharaman Swaminathan - GTM360 Marketing Solutions - Pune | 18 October, 2016, 15:47

Just when risk analytics, geoloc and other technologies are maturing and reducing checkout friction while minimizing false positives, it's sad to hear that they might be banned by new SCA-RTA regulation in EU. 


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job title Managing Director
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Paul Thomas is Managing Director at Risk Decisioning and Analytics technology provider Provenir

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