Table of contents
The next big thing in digital will be one-tap user journeys that shrink decision-making activities down to a single moment of truth. This is all about abstracting complexity and integrating different players on a unified platform to bring the focus on fulfilling an immediate need. Something like a WeChat in China that allows users to send instant messages, shop online and apply for a personal line of credit - without exiting the app.
For small businesses, this translates into buy now and pay later, with installments that don’t derail cash flows. These loans, loaned out through suppliers - say Shopify - without a direct interface with a bank, are offered right when they are needed, i.e., at the checkout. Through an app that may bear no affiliation to a bank.
That’s embedded finance for dummies, and it blurs the lines of distinction between participating entities to make a transaction a sure-shot thing.
Why cookie cutters will not cut
At the intersection of finance and technology – embedded finance is making banking everything about businesses, and much less about banks. Businesses can access banking services as a utility without even thinking of a bank. Consider how people are now paying through Apple Watch or letting Alexa place orders. Technology innovation is pushing payments away from cards or cash to highly digitized biometrics such as fingerprints, voice, or facial features.
This brings hyper-individualized and contextualized offerings to the forefront. Banks or lending partners cannot use broad strokes to classify loan candidates anymore. They need to build foresight into their operations to know who needs the loan, when and where. They also need intelligence to bolster prediction engines to fend off defaults, fraud or churn.
There’s data for it – enough of it – with merchants and lending partners. Channeling it into meaningful revenue streams for stakeholders across the ecosystem will require expertise in data analytics, artificial intelligence, and machine learning to generate real-time actionable insights.
Connecting the dots with data and AI
The key is to humanize this data and put it in context that traditional decisioning models do not. When AI calls the shots, it brings in not just speed but accuracy into the mix. It widens the scope of factors that can be considered before offering financial aid, while ensuring core banking activities such as credit risk analysis, underwriting or governance/audit trails are accounted for.
For the lenders, AI simplifies credit decisions by drawing on a varied set of alternate data (tax invoices, device data, transaction volumes etc.) to assess credit worthiness. Especially in a B2B context, where underwriting is more complex with factors such as shareholder control, servicing capacity and industry risk. With a streamlined AI-driven pre-approval process, decline rates reduce and loan authorization rates get optimized.
AI enables suppliers to offer curated offerings with contextualized loans. It helps them bring depth to sales conversations with the right pricing and promotional offers. By providing ‘way to pay’ options, suppliers are more likely to conclude an ongoing buyer journey with a transaction. With advanced data analytics and deep learning algorithms, suppliers can predict when a buyer will place a repeat order and work toward making that journey seamless and quick.
Governance and maintaining audit trails get easier. AI can help banks enhance regulatory compliance by flagging alerts wherever pre-defined processes are not followed. All of which goes a long way in ensuring data security and integrity.
By helping set these guardrails, AI enables merchants or suppliers to offer effective pricing and promotions. It also helps businesses transact seamlessly, in real time and within context. Thus, making the case of embedded finance even stronger.
Download our E-book for a crash course on Embedded Finance and how your business can leverage it to improve user experience, drive repeat orders, and boost Average Order Value.