Banks, credit societies and other traditional financial services firms have been facing a growing challenge from the fintech small business lending sector.
The large role SMBs play in the economy makes them a natural customer for financial services. However, the fairly high volume of small business loans that represent a relatively low dollar amount creates a need for robust risk analysis. Many financial services firms have shifted away from small business lending to reduce their dependence on the market.
Technological innovation has established a framework for successful fintech small business lending by building processes around data analytics, simplifying risk management and streamlining loan processing.
Fintech small business lending innovation is driving alternative finance opportunities
Alternative lenders have been disrupting the small business lending space for some time, leveraging a combination of data analytics and a willingness to take on risks that traditional firms have been largely unable to match.
As their systems have matured, fintech small business lenders have shown an ability to use a wider range of data in risk management and accelerate their loan processing operations. These benefits can take away many of the pain points banks and credit societies have faced with small business lending, creating a more attractive market environment and widening consumer bases for small business loans.
Challenges In Small Business Lending
Traditional financial institutions typically divide small businesses into two categories:
- Micro enterprises generate less than $1 million in annual revenue and are typically seeking loan sizes less than $250,000, usually serviced by the branch network.
- Small businesses are companies with less than $20 million in revenue or $1 million or less in exposures, generally serviced by the business banking group.
Manual credit decisioning
Bank lenders typically request three years of financial statements (a requirement that can be met with unaudited financial reports or filed tax returns for the business) and complement that data with credit bureau input on credit utilisation and payment behavior.
Data is then applied in an internal scoring model, with attributes that assess the overall creditworthiness of both the business and its guarantors. The data entered into bank systems produces an internal risk rating, which can be benchmarked against external scores to drive credit decisioning.
This process is often conducted across a series of unintegrated systems, resulting in inefficient and inconsistent results. The credit information vendor market is fragmented, and there isn’t a single source of credit information to meet financial institution’s needs for quality data for small business lending.
Automated credit decisioning
Alternately, credit decisioning may be nearly completely automated, with results consigned to an algorithm that rigidly applies parameters and can result in a high number of rejections. This is particularly true when the owner or proprietor of a small business is used as a proxy for risk calculations.
Most extant scoring models are heavily weighted to consumer data and can lack credibility at levels above personal lending. Serious gaps in pertinent data analysis exist, leading to high rejections rates that require an exhaustive manual examination to override. This time investment is a luxury traditional financial institutions simply don’t have.
Fintech Small Business Lending Trends
Small businesses seeking easier access to loans have been turning to alternative lenders that are proliferating to fill the significant funding gap. Fintech lenders face their own set of challenges, including increasing regulatory scrutiny, but have many advantages over traditional banks.
Traditional lenders have opportunities to improve the small business lending process across the credit life cycle, by taking pages from the fintech small business lending playbook:
Streamlining information collection
- Traditional lenders may find it challenging to get an accurate picture of a small business’ creditworthiness, due to the fact that many small businesses don’t generate information in sufficient detail to make an informed lending decision. Fintechs have more effectively differentiated the level of information required to assess potential risk exposure, embracing new data sources to reframe the requirements for accurate decisioning.
· Updating processes
- Time-to-money is a significant pain point for borrowers. Fintech small business lending depends heavily on the automation of manual processes and the implementation of rule-based decisioning for more rapid decisioning on low-exposure loans. This allows resources to be focused on higher-value activities and risk management across areas of higher exposure.
· Upgrading infrastructure
- Cloud platforms and software-as-a-service (SaaS) systems can deliver an integrated solution for managing the whole of the credit lifecycle. This reduces the overall cost of ownership for hardware and minimises the IT staff required to keep systems updated, backed up, and secure. Data duplication can be reduced or eliminated, and customer-facing systems upgrades for efficiency and intuitiveness.
· Analysing data
- High-performing lenders use data consistently throughout the lending process. Meaningful points can be extracted from every dataset, to provide insights into key performance indicators and facilitate meeting audit and reporting requirements. Understanding what data is the most meaningful is key, as is having the right data structures and systems in place to capture relevant data and ready it for analysis. This entire process will be subject to regulatory scrutiny, with data privacy and protection as a priority.
What’s Next for Small Business Lending?
Banks can remain competitive in the face of fintech small business lending by implementing the following:
- Automation for standard processes in the lending cycle, including the collection and structuring of small business data gathered from financial statements, tax returns and other pertinent documents.
- Development of new and improved credit models that integrate data and factors beyond financial information, including the use of broader data sources to help optimise decisioning and improve outcomes.
- Implementation of rule-based decisioning tools to streamline processes, improving workflow functionality and maximising qualified lending opportunities.
- Mitigation of risk and exposure via business intelligence capabilities, enhanced early warning systems, and exhaustive portfolio monitoring.
Robust, accurate analytic models can streamline credit decisioning for improved customer experiences and a higher rate of qualified approvals.
Embracing new small business alternative lending models
Transforming the small business lending process requires banks and data partners to leverage new credit information solutions. Customer and prospect data must be collected, utilised, and leveraged to create credible, quantitative, validated credit decisioning frameworks. By redesigning how small business lending is facilitated and using alternative decision models to unlock formerly underutilised data, banks can access a major source of revenue and push back against the encroaching wave of fintech small business lending solutions.
Banks and credit societies currently have a rare opportunity to meet the challenges posed by fintech small business lending. An Accion Insights report notes that the ability to access alternative data through digital sources can help lenders perform due diligence with greater speed and precision. Financial services firms that move beyond their traditional loan products and take advantage of these digital operational models can capitalise on the changing market dynamics to make a strong play in the small business lending sector.
GDS Link resides at the crossroads of these trends. Our analytics and risk management technologies offer flexibility, ease of use, and access to diverse data sources that are driving innovation in the alternative small lending space. Our experience working in more traditional lending sectors allows us to understand what banks and credit societies need to take advantage of new opportunities emerging in the small business lending sector.