Article | February 24, 2020
This blog attempts to answer the following key questions at a high level: What could financial technology companies (fintechs) do to enable greater consumer or SME (customer) adoption of their products and services beyond early adopters? What could fintechs do to pull under-served and well-served customers away from incumbent banks and established providers?
Article | April 14, 2021
The rise of buy-now-pay-later in the consumer world provides a powerful example of what embedded finance at the point of need can do for brands.
Consumer debt ethics aside, B2C buy-now-pay-later providers can proudly point to the fact that implementing a BNPL solution can increase basket sizes for retailers by 20-30%. The concept, although around for years in various forms, has now been delightfully served as an option during the customer’s checkout process and the marketing has been simplified and on point.
Even as merchants must pay for the privilege of offering BNPL (keeping it interest free for customers) upwards of 4-6% per transaction, the benefits have been too good to resist.
Although let’s face it, regulation aside (there are significant hoops to become licenced in B2C finance), underwriting to consumers is easier than extending credit to businesses thanks in part for the need to understand factors such as business servicing capacity, shareholder control, bespoke pricing, industry risk and not to mention the exposure risks associated to loaning out more significant totals.
Therefore B2B lending speed and embedded innovation naturally lags behind B2C. That said, there have been remarkable steps forward over the last two years as more and more millennials and digital natives take over business buying decisions.
Existing B2B finance models
For many B2B suppliers or merchants, a referral programme to a business finance provider is nothing new.
The benefits can be obvious, especially as the alternative is to embark in to the dangers of offering trade terms and all the cashflow and collection troubles that come with it. By introducing business finance, suppliers can get paid upfront and those pesky risks evaporate.
When used effectively in a negotiation, financing offers can also allow suppliers to remove expected buyer discounts. Total order values and loyalty increase (as in the consumer world) and accessible finance even helps a buyer’s businesses become more successful — resulting in increased volumes of purchasing.
Still, referral programmes of this nature have their problems. Even with same day B2B funding available, finance applications can hold up the buying process.
This means suppliers who are keen to leverage the buyers peak interest, antagonising don’t close sales as quickly as they could and run the risk of the of the application declining or extra customer frustration and fatigue.
While the benefits for introducing finance in to the sales process still outweigh the negatives, more improvement in the experience can be achieved.
Imagine a situation as a B2B supplier or merchant where you can provide a pre-approved finance offer straight to eligible businesses through your own CRM.
This level of proactivity provides you with a powerful tool in drawing in loaded sale opportunities from customers who are primed and ready to transact. The advantage over your competition is significant as your customers already know they can painlessly purchase from you once they’re ready for more stock.
A new revenue stream also opens for your business as your FaaS provider hands you commission for each loan a buyer takes.
This example highlights the power of pre-approving customers in a proactive capacity. For this level of FaaS to work a customer permission gate is required, either as part of a customer onboarding/sign-up process or through invitation.
With a white-labelled or co-branded experience, buyers can securely connect via open banking through a third party (FaaS provider) and be reassured that no visibility of this data given to their supplier. Other business data sources (e.g. payment or accounting software) can also be connected at this point.
Through providing these connections through the supplier or merchants portal, buyers can sit back with the knowledge that they will be notified if and when they qualify to purchase goods or services on finance. Once pre-approved, sellers can push a message to their buyer from their CRM system along with any sales pitch or offer they like.
Convincing buyers to give this connection permission in the first place is a challenge that clever marketers and UX specialists will need to overcome.
Embedded at the point of need
Again, we only need to take BNPL as an example of an embedded finance solution served beautifully at the point of need. Buyers, motivated at the time to transact, are more likely to have the determination to apply for finance within the same CX process.
Unlike traditional loan referral schemes that remain disjointed with long and possess awkward handover points, through FaaS, an embedded financial product is especially attractive when done right. It can merely feel as a simple ‘way to pay’ option in the buyer experience. No application forms; no double entry of business data; no waiting.
The challenge therefore is to create seamlessness, integrated and quick decisioning. Again, still a relatively tall order for B2B finance especially the higher the loan total is (despite advances in innovation and same day funding).
The potential for more instant decisioning will need to be enabled through a) improvements in API sharing and open finance analytics to confident business underwriting and b) buyer adoption on online services and platforms.
This later point can already be seen in the growing gulf of funding accessibility between online and manual powered businesses.
For example an eCommerce buyer, generating rich volumes of data through online sales and excessive B2B Software-as-a-Service (SaaS) use has the perfect data points that can be integrated in to support lending applications. In contrast, a blue collar business, with a limited online presence and who only drops a shoe box full of receipts to their accountant every 90 days, will find it more difficult to provide the data required for quick decisioning.
The new normal
Embedded B2B finance is here to stay and serious FaaS providers are emerging.
More and more suppliers and merchants will be leveraging the power of this new technology trend to create a more successful, loyal and satisfied buyer. Through proactive pre-approvals, B2B sales teams can be armed with more empowered conversations and unique advantages over the competition.
Alternative business lenders operating as an embedded finance FaaS will also benefit by leveraging the scale of the B2B supplier and merchant channel, accessing more businesses through wider mass distribution.
Soon, simply plugging in an embedded finance solution in to your existing CRM system will become a painless experience. B2B lending products of the future, through speed of underwriting and integrated data points, will be commonplace as an attractive option during the sales process.a
Article | May 26, 2021
The enormous amounts of data accessible to banks and their high demand for forecasting make the financial industry a perfect area for machine learning (ML) to shine. In this article, we explore the current applications of machine learning in banking when it comes to risk management, define its challenges and provide a future outlook.
Credit Risk Management
For the past few decades, banks have mostly used logistic and probit regression models for credit risk assessments and internal risk management. However, all conventional models inherit the same flaw — they predict outputs based only on linear relationships between input variables.
This limitation was exposed in the catastrophic 2008 housing market crash. Although the crisis’s negative consequences have been multiplied by uncontrolled sales of credit default swaps and other complex financial instruments, the fundamental reason for failure was in the inaccurate credit risk model.
In the aftermath, with the intent to force financial institutions to provide more detailed reports, The Federal Reserve’s CCAR now requires banks to account for more than 2,000 economic attributes. Consequentially, this also led to other regulating authorities introducing new standards that improve supervisory data quality and reporting.
At the same time, with the proliferation of banking apps, social media, and digital communication overall, financial institutions now collect lavish amounts of unstructured data. If gathered and processed correctly, these new datasets can help gauge critical insights for a wide range of banking operations.
This is where machine learning comes into play. More advanced non-linear approaches to credit risk modeling including neural networks enable banks to make predictions with a previously unseen level of accuracy and granularity.
The utter superiority of machine learning over traditional credit risk modeling approaches comes at the cost of the prevailing ‘black box’ problem. While we can decide to trust ML algorithms based on statistical evidence of their feasibility, current regulatory constraints won’t allow it to happen.
However, machine learning can still be used to a great extent while being regulation-compliant. Even simple linear machine learning approaches still yield more accurate results than conventional ones. Many banks also use unsupervised machine learning methods to explore data, while using traditional classification and regression models to make predictions.
Fraud Management and Surveillance
Nowadays, the majority of banks’ fraud detection systems use rule-based approaches. This causes banks to deal with a significant number of false positives, forcing them to spend inordinate amounts of resources to distinguish meaningless behavioral deviations from real threats.
The ability of machine learning to capture subtle trends and uncover non-linear relationships allows banks to get a complete picture of a client’s activity and significantly lower the probability of false positives. For example, by integrating ML into its fraud detection model, Danske Banks managed to reduce false positives by 60%.
Similar to many other AI-based solutions in the financial space, the biggest adoption hurdles concern regulations and the unexplainability of AI systems. For example, depending on the jurisdiction, banks are often unable to provide developers with sensitive information related to past breaches. Next, the outputs of unsupervised monitoring systems sometimes can’t be explained, which makes them non-compliant.
However, financial institutions have found a way to at least partly leverage the power of ML for fraud management. A fraud prevention system’s alerts will still be triggered by rule-based models, but the integration of an ML algorithm on top of them can allow adjusting surveillance methods to a person’s behavior fluctuations. Such ML models are typically less complex and explainable, which makes them applicable in a regulatory context.
Article | February 13, 2020
While tech giants tend to hog the limelight on the cutting-edge of technology, AI in banking and other facets of the financial sector is showing signs of interest and adoption even among the banking incumbents. Discussions in the media around the emergence of AI in the banking industry range from the topic of automation and its potential to cut countless jobs to startup acquisitions.