In today’s credit landscape that is becoming increasingly demanding, more and more financial institutions are realizing that harnessing the power of technologies like Artificial Intelligence, Machine Learning and Big-data Analytics can transform the way they look at their credit strategy and revolutionize their Accounts Receivable. And using machine learning to make sense of heaps of unstructured data and generating actionable insights can eliminate huge process inefficiencies in their credit management strategy, increasing the accuracy of their predictions and strengthening their business manifolds.
With proactive credit risk management strategies, businesses can protect themselves from exposure to many potential credit risks and the losses resulting from them. What’s more, these technologies come with a significant opportunity to improve processes at every stage of the credit life-cycle right from the credit negotiation stage, during performance monitoring, all the way to recovery.
How Technology Can Change The Credit Landscape
A closer look at how AI can bring this about, will help us understand its benefits better. Here’s how technological intervention streamlines each step of the credit management process:
1. Client on-boarding
Client on-boarding teams are responsible for conducting due diligence on each new client which involves getting answers to questions surrounding the company’s capital structure and needs. This information is gathered from clients directly or from external sources. The teams are also entrusted with the responsibility of making sure that all client records stay up to date. And all of this needs to be done with strict timelines in mind.
Unfortunately, manual KYC processes can be as time-consuming and expensive as they are inefficient. In fact, a research conducted by Finextra and Pega, a US-based software company, revealed that it can cost around $30k and up to four or five weeks to on-board a new client.
Automating can largely reduce these inefficiencies. It improves not just research efficiency, but also the quality and consistency of the data captured using both internal and external sources. All this information allows the organization to obtain a complete picture of a client during the on-boarding process.
2. Relationship Management
Relationship managers work with a varied clientele. Hence, it is of utmost importance that they have access to tools that can track a client’s needs, as well as keep themselves updated with any changes in the corporate structure or performance of the account.
Human resource is the most valuable component of any financial institution’s balance sheet. Something as trivial as a personnel change can sometimes result in the need to adjust a client’s limits or might provide an indication of a change in the company’s appetite for certain markets, or create the need for new products. The ability to gain these early insights into any new development can prove to be extremely beneficial to relationship managers.
Using manual labor to build and maintain a database of key people is extremely time-consuming and ineffective. However, getting an AI -based system in place could not only automate the collection of this information but also analyze any changes in the organization and notify the parties involved, in advance. And by actively tracking and analyzing relevant client information, these technologies provide an early warning system that can help a business tighten its credit risk management strategy, and take swift actions to reduce its exposure to newer risks as soon as the profile of a client changes.
3. Credit Analysis
The credit analysts in a firm need access to a client’s risk status and they arrive at this status using accurate internal and external financial ratings and a few details about the client that aren’t so readily accessible. This information could be a change in employment status, details of payment disputes and other information that gives an insight into the risk profile of the customer.
AI can play quite an important role in this information gathering process. It can help gather information that is much more reliable and from a wide variety of sources – gauging market opinion on a client’s current rating using sentiment analysis, for instance. This provides a much more efficient way to rate and re-rate clients based on the quality of information gathered from a range of sources. And through better visibility and early warning & monitoring tools, AI enables credit analysts to develop a transparent and robust risk assessment strategy.
4. Collateral Management
The collateral management team in an organization needs access to information on collaterals for it to be able to efficiently assess their ownership, value and availability. It needs the right tools to be able to perform internal assessments on them and develop an understanding of their dynamics and make sense of the transactions around them. They also need to be able to establish processes that support the valuation and sale of collaterals in case of default by a client.
By deploying cognitive tools, the team can gauge market conditions and monitor the changes in collaterals more proactively, which can give them a bird’s eye view of the accounts held. This will not just make the company’s capital efficiency estimates more accurate but also help them have an early warning system in place.
All Things Considered
Automation is able to deliver astounding results in many crucial aspects of the credit management process like client on-boarding, ongoing record maintenance etc. and the future for more such enhancements looks promising with continuous innovations in the fields of Artificial Intelligence and Machine learning, especially surrounding credit risk.
It won’t be long before all major players in the finance space embrace this welcome change and the manpower-intensive and manual handling of these tasks becomes a thing of the past.