Automated, data-driven credit decisioning is streamlining the operations of financial institutions of all sizes. AI-powered, next-generation credit-decisioning models and credit risk analytics solutions are being widely applied by both traditional lenders like banks and alternative lenders.
Yet one important sector is still underserved in the automated credit risk analytics revolution: SME lenders. Many of these still largely rely on manual underwriting by in-house credit experts. To keep up in an increasingly crowded market, SME lenders need to respond faster to credit applications. They need to minimize the work associated with application processing, maximize application acceptance rates, and lower the risk of default. A data-driven decision-making model enables SME lenders to:
® Automate – Turning the manual process of reviewing and accessing credit applications and decision making into an automated process, reducing the time it takes to respond and the amount of resources required
® Optimize – Using bespoke analytical scoring to make better and smarter credit decisions - approving more applications and reducing default rates
® Personalize – Offering a unique credit product to each customer based on their specific financial status and cashflow
In this post, we’ll take a deep dive into some of the challenges involved in implementing a data-driven credit decisioning model - and how SME lenders can overcome these and enjoy the benefits of the credit analytics revolution. We’ll examine how cutting-edge, field-proven technology is automating and optimizing the credit decisioning process – helping SME lenders incorporate diverse data sources and offer more competitive funding options to the SME market.
So Much Data Available
With a wave of new data sources available, the SME segment is increasingly data-rich. Today’s SME lenders can draw on diverse data from sources like:
1. Open banking data – Open banking standards offer SME lenders seamless and direct API-based access to SME banking, transaction, and other financial data.
2. Credit bureau data – SME lenders have access to a wealth of data from credit bureaus or agencies that gather account information from various creditors.
3. Government company filings data – SME lenders can draw direction from financial statements and reports filed in any company registry worldwide.
4. SME accounting systems – Open accounting enables SME lenders to directly access SME accounting platforms in order to better tailor their financial services.
5. Internal data - SME lenders frequently have a repository of internal data on potential credit candidates - from communication history with the customer, past loans and sector expertise - which can also facilitate data driven credit decisioning.
How to Leverage the Data?
SME lenders need to effectively use all data available to offer better, more personalized credit products with lower risk. They need to make better decisions on who to provide credit to, and how much.
This means lenders need to identify credit risk (probability of default) to determine the loan pricing. It also means they need to analyze predicted cashflow and its potential future fluctuations, so they can optimize credit terms and size for each borrower.
Yet there’s a hitch. The tsunami of data available to SME lenders is raw data, sometimes unstructured. Deriving insights from such data is a challenge – deriving insights at scale is nearly impossible using traditional or manual methods.
What’s more, the data sources available do not include performance data – data based on a specific customer’s patterns of repayment of credit products, including defaults. In order for a statistical model to analyze performance data and identify risk indicators and scorecard, lenders need enough data – which can take years to collect. This makes it tough to project chances of default without very sophisticated, processing-intensive modeling.
The Next Step for SME Lenders
To take advantage of the credit analytics revolution – enjoying the speed, profitability, and personalized credit offers – SME lenders need a new data-driven approach. They need technology that can take all the raw data, create smart features and implement models that are tailored to each specific lender.
When performance data is lacking, hybrid models that combine expert know how, benchmarks and statical analysis can be a good starting point. Once enough performance data is accumulated, lenders can apply machine learning to constantly improve and optimize these models.
Leveraging data-driven credit decisioning, SME lenders can offer more competitive credit products, review more applications more quickly, and respond faster to SME customer credit requests. They can more effectively harness the power of data available, and create a streamlined process for timely and accurate credit predictions.
To mine the emerging wealth of SME data sources without hiring a data science team, more and more SME lenders are considering automated, AI-powered, data-driven credit decisioning solutions, that can analyze raw data and offer a hybrid model, like that offered by Paretix. The credit analytics revolution in SME lending is here – and we’re powering it.
