We met with Marwa Nassef Head of Risk at Cashew Payments and had this interesting conversation with her.
What would you say is the Significance of technology for agility?
Technological agility is the firm’s ability to quickly react to technological changes
With technology, we see creation of new products in response to changing times impacted by market conditions. A perfect example of this has been Covid-19, which kickstarted the digitization of a lot of industries such as F&B, FMCG and of course banking and lending. As this become digital, there is increased consumer expectation, as well as a growth in competition, and therefore technology has become the backbone of successful businesses.
So, in my line of business; banking, I have seen a real development in the role of Risk in businesses. Whilst it has always been a key component in any finance, tech or data industries, Operative Risk Management is no longer only about mitigation or being a gate keeper, but now about driving value & revenue to the company.
With newer technologies, we can swiftly change, enable, or adapt our policies and processes as we get a hold of newer data sources and more dynamic modular platforms.
Do you think there is an impact on time element for market penetration and tech teams dependencies?
Traditional technology monopoly by technical and engineering teams created unnecessary stalemates. New-age platforms however, decentralized product development!
The modifications mentioned earlier, when it comes to updating or changing our models, will require little to no code – post the initial model setup and integrations of such New-age Platforms.
Technology allows us to tailor how we cache our data and hence reduce hefty invoices on paid data calls that we run to test our campaigns or tests to existing portfolio.
The integrations are faster, smoother, and in modular platforms, we can use these integrations for all our lines of business. The same platform that can be used to derive lending score and decision, can be used to define our marketing and business development strategies.
This means all the data we are getting now, can be used across the business with a onetime set-up requirement from our teach team, we aren’t constantly relying on them for adjustments, data pulls and analysis, instead we have bespoke platforms that provide us exactly what we need.
And what about visibility?
Each data source and its API can be viewed and adjusted within our advanced middlewares . With 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 quickly and safely adapt, continuing to optimize our policies. All of which significantly reduces our customer acquisition costs while improving our market footprint and approval rates.
We also run batch channels – as a workflow – whether at defined times or on demand, to recalculate score or turn out a behavioral score that then impacts the decisions we make internally.
This visibility allows us to understand when any manual intervention is needed, which with the sophistication of risk platforms these days, is rare. This leads to businesses, such as BNPL’s like Cashew, able to make strategic decisions in near real-time which nowadays, is required for customer satisfaction and providing a great user experience.
We can also adjust contingency plans – and reduce our provisions using dynamic platforms as such.
With reducing acquisition costs, operational variable costs, and impairment rates, all directly and positively impact out bottom line as a Fintech in niche markets, with less mature data than other regions.
How do you see the customer experience evolving in that manner ?
Customers’ expectations is now instant , instant , INSTANT!
With the pandemic shifting almost everyone’s lifestyle and life choices, and as both millennials & Gen Z started to fill the work and customer space with very different aspirations, everyone is now expecting businesses to understand their specific, individual needs
Consumer’s previous experiences with lending, required submission of endless documentation, that was assessed at different levels and by different departments, before a final decision was given.
True, scorecards existed, and huge databases were there, but, to the customer, they just had to come in during office hours, submit documents and wait for days to get their “approval”. Minimum ticket sizes of credit cards or loans were usually on the higher side mostly to cover for acquisition costs.
Whenever a lender provided micro-sized loans, it was packaged with incredible fees and charges, since it was never part of traditional lenders risk appetite.
Any enhancements, prior to technological advances, were based on internal processes and workflow shifts, but never the data points to assess. The shortest I have witnessed for “PRE-Approval” was 30 minutes, or of course the loan calculators on bank websites, and they were only based on information declared by applicants that will be still validated – as part of the T&C’s – which again, will lead to a day or more – based on timings for document submissions & validations.
With open banking and API’s, Banks have upped their credit and lending game, within their own risk appetites and ticket sizes.
Now however, with Cashew and other BNPL players, the use of newer technologies enables us to get new and significantly higher sets and data points to analyse, and feed into high speed risk decisioning systems, using our own scorecards that evolves with AI. This has created a significant boost in inclusion and thus happier consumers. The customer can get what was called a micro-sized lending, for a specific, and visible purpose, and be done with it in less than 90 days. This all happens with proper applicant permissions at the click of an icon.
We are confident in these new technologies because they can track and keep up-to-date on customers spending and payment behaviour on an individual basis as well as a collective level and therefore we are capable of learning and using that learning automatically, allowing for new variables to emerge and be counted into the screening of new consumers and retaining existing ones.
