Predictive Analytics in Business Finance

In an ever-changing landscape of business, predicting the future becomes tough, especially where business finance is concerned since there are many factors at play which can predict whether the lending will be successful or not. Organizations are increasingly turning to predictive analytics to navigate the complexities that come with business financing. By leveraging historical data, advanced algorithms and machine learning, organizations can gain insights into the SME borrower and enhance their decision-making regarding lending. We see that the UK business finance landscape has undergone shift with regards to both borrowers and the lenders:

• 1. Improving access to SME credit data: According to FCA Credit Information market study, the UK has a relatively advanced credit information market, comparing favorably to many other countries both in terms of depth of information.
• 2. More SME lenders adopting analytics and machine learning: There has been increase in number of SME lenders adopting analytics and ML algorithms to analyze their transaction data, real-time credit assessment and personalized lending recommendation.
• 3. Increase in Digital Transformation: Due to increased competition among lenders, there has been a race towards digitization for enhancing customer experience. Due to this, traditional high street banks are also adopting new-age technology due to competition given by challenger banks.

There are several applications of predictive analytics in business finance, some of which lenders are already taking advantage of:

• 1. Risk Assessment and Management: This is very important with respect to predicting a likelihood of default for an SME borrower. Credit scoring models are used to give insights into borrower behavior and fraud detection is used to identify anomalies in the transaction data, thus helping businesses safeguard against losses.
• 2. Automation of processes: Lenders can take advantage of predictive analytics in automating processes like loan approvals, determining optimal interest rates. Another area where automation can be done is document processing which can save underwriters time and improve decision making.
• 3. Improving compliance: Predictive Analytics helps organizations simulate stress scenarios, test compliance with regulations and can better prepare organizations to adapt to changes in regulatory compliances.
• 4. Personalization of Services: Through predictive analytics, lenders are able to have insights regarding borrower behavior. By anticipating future needs, personalized experiences can be provided to the borrower in terms of the loan product which fits them.

The fundamental process of predictive analytics starts by collecting the data followed by analyzing and then interpreting the data. The statistical and machine learning methods are used to detect future movements of data points, outliers in the data and deriving insights which can be used to take a decision by the organization.

How can Predictive Analytics make a difference:

• 1. Improved Operational Efficiency: One of the areas where organizations incur cost is manual processes whether it be in handling documents, making decisions. Predictive analytics can automate manual processes, streamline document flow and enhance decision making efficiency through improved risk assessment, fraud detection.
• 2. Competitive Advantage: Lenders using predictive analytics can gain a competitive edge with their competitors in terms of first mover advantage and market share.
• 3. Improved customer satisfaction: Lenders, by offering personalized services through predictive analytics can enhance borrower satisfaction leading to increased customer lifetime value.

Challenges with Predictive Analytics

• 1. Data Quality:

  This is often a challenge in predictive analytics since the accuracy of the models are dependent on the quality of data being analyzed. For example, skewed data can lead to models giving a biased prediction which can lead to poor decision making when it comes to lending.

• 2. Integration with existing systems:

  Another challenge is the integration of predictive analytics tools with existing transaction systems of lenders which can be complex.

• 3. Complexity of Models and Algorithms:

  Predictive Analytics models are quite complex and deploying them requires experts which requires lenders to hire and train manpower to build and maintain these models.

Conclusion

Predictive Analytics is transforming the business lending landscape through data-driven decision making and can enhance efficiency of organizations. Digilytics offers a sophisticated solution through RevEL which helps lenders to operate with unprecedented speed and efficiency. As the SME sector continues to navigate economic fluctuations and market demands, the ability of lenders to innovate and embrace data-driven, adaptive credit policies will be enhanced by Predictive Analytics.

Author: Akshat Dev, Partnership Development Strategy at Digilytics AI

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