In SME lending, trust plays a critical role in defining multiple parameters of engagement between borrowers and financial institutions. A credible small business gets faster access to working capital, better pricing and interest rates, and flexible repayment terms. But for small businesses in the US, conventional credit analytics and risk assessment metrics cannot lead to an informed credit decision. New businesses less than three years old often lack traditional cash flow data and conventional credit ratings to help lenders conduct precise credit risk analysis.
While it may be a challenge for small businesses to gain access to working capital, counterparty regional banks face the actual heat. Due to traditional relationship-led lending practices, manual cash flow data aggregation, and siloed technologies, small community banks continue to show resistance in lending to thin-filed SME borrowers.
According to a recent BCG & Biz2X whitepaper, the SME funding gap in the US ranges anywhere between $350 billion and $750 billion. As a result, the estimated market opportunity in SME lending amounts to nearly $1.7 trillion. Large banks have realized this opportunity. They are leveraging digital transformation, artificial intelligence, and alternative credit data to maximize their SME lending market share. It also reveals that large banks that have partnered with fintechs for more than just credit analytics can disburse a small business loan in less than 10 days.
Meanwhile, regional and small community banks that use traditional credit analytics and risk management processes fall through the cracks due to poor visibility into cash flows. It is a critical tool for asset managers for creditworthiness validation, accurate underwriting, and near real-time decision-making. It gives fast, accurate, and unbiased insights from cash flow data, which has become an important asset for generating alternative credit data in SME lending.
Why Cash Flow Is the New Credit Score
Cash flow analytics can bridge the gap for SMEs that lack adequate financial documentation or formal credit histories through alternative credit data. Alternative credit data primarily relies on cash flow analytics to create credit scorecards beyond the credit bureau insights. In simplified form, it provides a clear picture of how, when, and why the money moves in and out of their business system. By using a business lending platform to collect, analyze, and manage this data, regional banks can gain insights into daily, weekly, monthly, quarterly, and even annual cash flow patterns.
Here’s what effective cash flow analytics can reveal:
- Visibility into inconsistencies of payment cycles
- Behavior of the borrower during financial stress periods
- Impact of seasonal trends on the SME’s credit structuring
- Any discrepancies in the furnished vs actual revenue activities
- A clear picture of available cash reserves to handle debt during extreme situations
- Personal spending behavior of the small business owner
While cash flow data can strengthen credit analytics, if done manually, it can also pose certain challenges:
- Incorrect inputs from bank statements, Excel sheets, ERP reports, and other document formats can expose your risk teams to incorrect data sets.
- Even one miss in OCR or data parsing of financial numbers can position the borrower in a false negative state.
- Duplication of entries may trigger inconsistencies in every stage of the loan lifecycle.
- Misunderstanding the seasonal dip in cash flow as a larger issue and vice versa.
These challenges can make a regional bank prone to risks like:
- Incorrect scenario analysis, loan disbursal, pricing, reconciliation, and even compliance audits.
- Missed warning signals can sanction loans to probable defaulters and fraudulent threat actors.
- Poor decisioning can trigger non-compliance with fair lending regulations, resulting in hefty penalties.
- Degradation of trust among customers and reduced reputation among industry peers.
The best of credit and risk analytics tools can perform only as well as the input your credit analyst, risk manager, or loan officer provides them. Correct cash flow data input is imperative for early warning signals and effective portfolio management. However, financial institutions must also ensure high data integrity of the financial data they collect, process, and share with third-party services. Automation of cash flow data collection and processing cannot compromise on consent, security protocols, and transparency of usage.
How to Achieve Maximum Cash Flow Data Integrity
In a digital-first world, manual risk models lag far behind. Whether you’re stress testing market conditions, creating market intelligence datasets, or evaluating potential risks, adopting digital lending technologies is the best way to build the right cash flow and credit analytics workflow.
Here’s how a business lending platform optimizes cash flow-related tasks:
Automated Document Management
A business lending platform comes with pre-built integrations with credit bureaus, bank accounting software, management information systems, and third-party private financial services. It securely pulls all relevant financial documents from multiple systems, converts them into a unified format, extracts cash flow data, and publishes it on a visually simplified dashboard.
It helps credit management teams reduce the document gathering, sorting, and extraction effort and process timelines significantly. Automation also prevents any oversight misses or manual entry errors, improving the accuracy and correctness of inputs. It also prevents duplication of data and highlights errors or delinquencies in input information, so that human experts can escalate or take action accordingly.
Real-Time Credit Analytics
A business lending platform is highly configurable to a financial institution’s unique credit policies, prescreening eligibility criteria, and other loan decisioning factors. After automating the documentation, the digital lending platform leverages artificial intelligence capabilities and machine learning algorithms to generate credit reports tailored to your conditions.
All of this is done in near-instant time without any human intervention, freeing up the bandwidth of risk management teams to cater to more loan applications. AI also helps forecast probabilities of default and flag potential fraudulent applications. With fast, comprehensive, and simplified visuals of cash flow data like burn rate, liquidity ratio, or seasonality index, underwriters can make smarter lending decisions while promoting inclusivity for the underserved small businesses.
Built-In Compliance Capabilities
A business lending platform, offered by providers like Biz2X, comes with in-built capability of decision logging, audit trailing, and compliance reporting. By logging every lending decision, from loan origination and disbursal to loan servicing and collections, the software significantly reduces the need for manual data entry work. AI-powered business lending platforms also automatically update to the emerging regulations, eliminating the need to manually update policies. It helps minimize chances of bias, unfairness, and discriminatory decisions by alerting the risk teams about the inconsistencies on priority.
Relationship managers can boost digital engagement with borrowers, and underwriting teams can focus on evaluating the credit decision, improving accuracy, customer satisfaction, and operational efficiency. An effectively utilized compliance logging and reporting system can further reduce the need for resource allocation on manual data entries while minimizing the chances of data duplication.
The Right Strategy to Maximize Cash Flow Data Advantage
Implementing a business lending platform equipped with credit analytics may not fulfill the expectations of modern small business borrowers. While they demand fast, remote, and transparent decisions, they also expect human-driven relationship management. Regional banks in the US thrive on their strong relationship-building abilities.
Additionally, the adoption of automation and artificial intelligence in lending face multiple concerns:
- Legacy disruption due to digital transformation can force some or all functions of the existing lending processes to halt. Here, the business lending platform vendor’s expertise and experience working on a project like your financial institution will impact the extent and minimalization of disruption.
- AI tools develop capabilities from the data they are trained on. If the AI credit analytics tool your vendor offers is trained on data that is biased, the output of the tool may amplify bias, miscalculate credit scoring, and trigger non-compliance to fair lending regulations. Look for compliance practices addressing the Equal Credit Opportunity Act (ECOA), which provides specific measures to mitigate bias in credit decisioning.
The best-case scenario is to complement human experts with advanced cash flow analytics and lending technologies that work seamlessly across the loan lifecycle. It will help your financial institution reduce processing time, boost analytics solutions’ accuracy, and ensure compliance with regulatory requirements. By doing so, the control of your lending decisions remains at your end, while the business lending platform becomes a collaborative partner in improving efficiency and lowering operational overheads with the same number of resources.
Final Takeaway
Regional and small community banks in the US are sitting on a nearly $1.7 trillion lending opportunity. Alternative credit data backed by strong cash flow and credit analytics from financial statements can help them overcome the hesitation of lending to thin-filed new small businesses and expand their reach to more SME clients without increasing exposure to risk. It will also help small banks and credit unions build case studies to improve credibility in the industry and gain healthier borrower applications.
Ready to Implement Cash Flow and Credit Analytics for your Financial Institution?
Schedule a customized demo of the Biz2X business lending platform and experience it decode cash flow data into actionable insights live.
FAQs about Credit Analytics
1. Why is cash flow analytics important in SME lending?
Cash flow analytics offers real-time insights into a business’s ability to repay, even when traditional financials or credit histories are unavailable.
2. How does credit analytics differ from traditional credit scoring?
Credit analytics uses broader data, like transaction behavior and cash flows, while traditional scoring mainly relies on historical credit bureau reports.
3. Can regional banks use credit analytics without a full digital transformation?
Yes, credit analytics platforms can be modular and integrated gradually into existing loan origination and core systems.
4. How often should a bank audit its cash flow analytics engine?
The banks are supposed to have quarterly auditing, and whenever there are significant changes in models or data sources.
5. What makes credit analytics trustworthy?
A trustworthy cash flow analytics process relies on clean, timely data, transparent models, and human oversight at critical decision points.
