Marwa Nassef’s perspective on the impact of technological advancements on BNPL

We met Marwa Nassef Risk Manager at cashew payments and had this interesting conversation with her.

How important do you think technology is to agility?

Technological agility is the ability of the company to react quickly to technological changes

With technology, we see the creation of new products in response to changing times impacted by market conditions. A perfect example of this has been Covid-19 which has kickstarted the digitization of many industries such as F&B, FMCG and of course banking and lending. As it goes digital, there is an increase in consumer expectations, as well as a growth in competition, and so technology has become the backbone of successful businesses.

So in my line of business; bank, I noticed a real evolution of the role of Risk in companies. Although it has always been a key element in any finance, technology or data industry, Operative Risk management is no longer just about mitigating or being a gatekeeper, but now about generating value and revenue for the business.

Thanks to new technologies, we can quickly modify, activate or adapt our policies and processes as we acquire new data sources and more dynamic modular platforms.

Do you think there is an impact on the time element for market penetration and technical team dependencies?

The traditional monopoly of technology by technical and engineering teams has created unnecessary dead ends. New-age platforms however, decentralized product development!

The previously mentioned changes, when it comes to updating or modifying our templates, will require little to no code – post initial template setup and integrations for these New-age platforms.

The technology allows us to adapt the way we cache our data and therefore reduce the heavy bills on the paid data calls we run to test our campaigns or test the existing portfolio.

Integrations are faster, smoother, and in modular platforms, we can use these integrations for all of our lines of business. The same platform that can be used to derive the score and loan decision, can be used to define our marketing and business development strategies.

This means that all the data we get now can be used across the company with a one-time setup requirement from our team of teachers, we don’t constantly rely on them for adjustments, data extractions and analysis instead, we have bespoke platforms that provide us with exactly what we need.

And what about visibility?

Each data source and its API can be viewed and adjusted in our advanced middlewares. Through rapid testing, we can instantly identify data sources with immense positive impact as opposed to those dragging the risk model. With this knowledge, we can adapt quickly and safely, continuing to optimize our policies. All of this dramatically lowers our customer acquisition costs while improving our market presence and approval rates.

We Also run channels in batches – like a workflow – whether at set times or on demand, to recalculate the score or produce a behavioral score which then impacts the decisions we make internally.

This visibility allows us to understand when manual intervention is needed, which, with the sophistication of risk platforms these days, is rare. This allows companies, such as BNPL and Cashew, to make strategic decisions in near real time, which is necessary today for customer satisfaction and to provide an excellent user experience.

We can also adjust contingency plans – and reduce our provisions by using dynamic platforms as such.

With the reduction of acquisition costs, operational variable costs and depreciation rates, all directly and positively impact results as a Fintech in niche markets, with less mature data than in other regions.

How do you see the customer experience evolving in this way?

Customer expectations are now instant, instant, INSTANT!

With the pandemic changing the lifestyle and lifestyle choices of almost everyone, and as millennials and Gen Z have begun to fill the workplace and the client space with vastly different aspirations, everyone now expects companies to understand their specific and individual needs.

Previous consumer loan experiences required the submission of endless documentation, which was assessed at different levels and by different departments, before a final decision was made.

Sure, the dashboards existed and huge databases were there, but for the client, all they had to do was show up during office hours, submit documents, and wait days for their “approval”. “. Minimum credit card or loan sizes were generally higher, mainly to cover acquisition costs.

Every time a lender gave out micro loans, it came with incredible fees and charges because it was never part of the risk appetite of traditional lenders.

All improvements, before technological advancements, were based on internal processes and workflow changes, but never on the data points to be assessed. The shortest I’ve seen for “PRE-Approval” was 30 minutes, or of course the loan calculators on bank websites, and they were information based only declared by applicants who will still be validated – as part of the T&Cs – which, again, will lead to a day or more – depending on the deadlines for submission and validation of the documents.

With open banking and APIs, banks have upped their credit and lending game, within their own risk appetite and ticket size.

Now yet, along with Cashew and other BNPL players, using newer technologies allows us to obtain new and significantly higher data sets and points to analyze and feed into high-throughput risk decision systems , using our own dashboards that scale with AI. This has dramatically boosted inclusion and therefore happier consumers. The client can obtain what is called a micro-loan, for a specific and visible purpose, and be finished in less than 90 days. This all happens with the proper candidate permissions at the click of an icon.

We are confident in these new technologies as they can track and keep abreast of customer spending and payment behavior on an individual basis as well as on a collective level and therefore we are able to learn and use this learning automatically , allowing new variables to emerge and be taken into account in the selection of new consumers and the retention of existing consumers.

About Glenda Wait

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