3 Ways Banks Can Work With Digital Lenders

Introduction

A World Economic Forum report issued in 2015 made the prediction that ‘incumbent financial institutions will come under attack in areas where the greatest sources of customer friction meet the largest profit pools.’

The report focused on Small to Medium Enterprises (SMEs) and cites that bank failures resulting from the financial crisis, lack of modern credit modeling, increased cost of capital and the inability to effectively serve SMEs on a wide scale have made banks vulnerable to a new breed of technology focused competitors known as ‘digital SME lenders’.

 

 

Source: Deloitte, ‘Marketplace Lending, 2.0’

 

Fast forward 3 years: while digital SME lenders have certainly taken market share, banks have been able to fend off a complete disruption by digital lenders by disrupting themselves and becoming more open to adopting emerging technologies.

In the same three years, another realization has taken place:  both banks and digital lenders each have limitations on how best they can serve SMEs. As it turns out, potentially sharing each of their strengths can prove to be the path forward to better servicing the SME global community and help to reduce the staggering $6.3 – $7.8 Trillion global SME credit gap.

While the title of this document looks at opportunities from the bank’s point of view,  the goal is to show how reciprocal partnerships can work to the effectiveness of both parties, especially in light of new and emerging trends in SME technologies.

1. More, More, and New Data

Making better use of newer, and diverse pools of structured and unstructured (alternative) data to broaden a bank’s  SME scoring methodology can widen a banks list of underwriting prospects and can potentially increase the predictive power of their scoring model.

Many SMEs don’t carry traditional credit data, which can immediately shut them out of traditional bank scoring models and subsequently, bank credit.  Digital SME lenders are well known to have adopted scoring methodologies that are more nimble and capable of quickly assessing alternative forms of data to help make better, faster credit decisions and across a more diverse audience.

According to the GPFI’s Report: Alternative Data Transforming SME Finance (2017), taking advantage of additional modern data sources can be used to make better risk decisions. Extracting and validating alternative, unstructured data such as social media data,  can be a powerful asset in evolving a lender’s SME risk-scoring accuracy.

 

Source: GPFI, IFC, World Bank

 

Combining alternative data with traditional data and then refining the scoring with continuous testing, learning and adoption can create a modern risk-score that becomes progressively more precise.

Of course, alternative data and subsequent risk scoring analysis requires quality data scientists and a scoring technology process that involves underwriters, and machine-learning solutions to ensure a proper output of scoring calculation and signal extraction. Not to mention, obtaining SME data must fit in with privacy regulations (such as GDPR), which smart contracts and DLT frameworks are better suited for.

2. Collaboration

FinTech partnerships are no longer shunned by banks. In fact, many of the larger, global banks have established tactical and strategic FinTech relationships in varying degrees. However, the connectivity between SME digital lenders and the legacy banks are still in early stages.

It is no secret that startups do not carry the same burden that banks do in terms of managing legacy system infrastructure, regulatory capital, operational costs, compliance costs, etc.. As such, digital lenders are free to focus more on technology solutions and can provide convenient, clean, products more quickly to the SME marketplace.

 

Source: IFC, World Bank, HBR.Org

 

However, banks are inherently more stable (as result of capital requirements) and often carry significantly important pools of data on SMEs (such as banking data, cash flow, etc..). Unfortunately, mining, extracting and refining that data across various silo’d bank systems can be a massive challenge.

Clearly, collaborative, meaningful and reciprocal relationships between digital lenders and banks can be advantageous to not only the two providers, it likely opens more credit opportunities for SMEs:

  • Banks can consider adopting technology provided by the digital lenders to better harness the SME data housed within their own legacy systems (as shown in the above quadrant).

 

  • Through strategic, ring-fenced relationships, banks can also leverage new and advanced Machine-Learning and AI powered risk scoring solutions that some of the digital lenders have already begun to master. The advantage of this approach is to obtain a faster time-to-market capabilities in extracting alternative data scoring and prevent these processes from being contaminated by a bank’s inefficient legacy infrastructure.

 

  • In return, banks can create meaningful SME reciprocal relationships with digital lenders, providing them with valued leads, and pre-qualified client referrals in the instances where certain products simply will not fit in their bank’s profile.

3. Inter-Operability

Major private DLT frameworks such as JP Morgan’s Quorum, R3’s Corda, Hyperledger Fabric and public frameworks such as Ethereum, are well into the process of institutionalizing their products with a robust list of participants, followers and adapters.

As the adoption of distributed ledger technologies (DLT) and other advanced, emerging technologies becomes more commonplace in the banking industry, we are anticipating the emergence of a new wave of DLT collaboration or framework inter-operability.

 

Source: Apla Blockchain Business Review

 

At some point, instead of having to pick sides and select silo’d DLT frameworks, banks will eventually be able to optimize all of the data components housed on most, or even all of the various frameworks.

The advantage of DLT inter-connectivity is that banks will be able to push, pull and share both golden-source, on-boarding and real-time risk data between these frameworks to reduce KYC / AML validation friction, increase payment instruction accuracy and speed, mange both systemic and idiosyncratic risk more efficiently, and optimize regulatory reporting, just to name a few.

The challenge with the above-mentioned  inter-operability, is that many regional banks are either not included or less involved in the established consortium network. In addition, and to the best of our knowledge – only premier SME digital lenders appear to be participating in bank sponsored frameworks (many at an arm’s length).

 

As such, we believe that one or a few new SME DLT frameworks (this time designed by digital lenders, for digital lenders), will finally be adopted to better serve and institutionalize SME data sharing between alternative lenders (in the same manner banks have done). This is a major focus of the Lendindex solution.

 

In Conclusion

Whether it be permission-less frameworks, such as Ethereum, new SME DLT frameworks, or bank sponsored permissioned frameworks, it will be advantageous for systemically important banks, regional banks, digital lenders, SMEs and regulators alike, that all participants engage in the evolution of SME data inter-connectivity.

The future of SME framework technology should be inclusive of all types of providers, for it to be truly transformational.

 

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