The application of AI technology is helping financial institutions to better understand their true level of credit risk by ‘wrangling’ meaning from vast, often unstructured data sources
Caution may be a most desirable quality for all successful bankers, yet it remains an inescapable fact that banking itself is among the most high-risk industries in the world. What could be riskier than lending money to people without ever knowing if you’re going to get it back?
There is a reward factored into each transaction, of course, but for banks there is always the inherent risk that a business or individual will not repay on time, and that the loan will become ‘non-performing’. This is the price that financial institutions pay for a business model whose very essence is risk.
Non-performing loans (NPLs) are one of the main sources of value destruction for banks, especially during times of economic crisis. Typically, a NPL is one that is 90+ days past due. This forces the bank to restructure the loan, ideally so that the borrower can resume making payments and the loan becomes ‘reperforming’ (albeit with a higher chance of defaulting again).
Then there is the best-case scenario, the performing loan, where repayments are made on schedule and there is no issue. Three different loan scenarios, each requiring varying degrees of time and resources to evaluate: for originators, who must validate the loan; for servicers, who must process the facility; and for buyers, who must conduct due diligence and understand the value of the portfolio they are purchasing.
As we explored in a previous blog, in order to effectively package and sell loans – a key feature of the secondary mortgage market – asset managers must be able to quickly and accurately identify any discrepancy between perceived and actual value. Where vast amounts of documentation and multiple file types are involved, this is easier said than done.
It is difficult and extremely time-consuming for humans on their own to try and extract meaning from hundreds or even thousands of diverse files and documents. After years of grappling with paperwork and developing complex in-house processes, financial institutions are now finding that one solution is to supercharge human thinking with the awesome power of artificial intelligence.
This is where Altada comes in. Our AI solution helps clients to unlock value from large, unstructured data sources – a sprawling mortgage loan file, for example – by extracting, wrangling, and analysing the components of multiple documents.
Reduce loan processing costs by 90%
For asset managers, this is eureka territory. Altada’s AI solution pinpoints the true value of assets by homing in on the data that really matters, then feeding it into the user’s own investment models for quick, accurate decision-making. Our technology has been shown to achieve over 90% accuracy, reducing the time it takes to process a loan portfolio to just 48 hours and decreasing the cost of processing a loan file by up to 90%.
One of our clients is a Texas-based investment manager who has seen its operational and cost efficiencies utterly transformed by the application of AI. Our platform helps them understand the true value of its portfolio by extracting and parsing the components of multiple documents, both structured and unstructured.
This investment manager then puts these components to work, using them, ultimately, to make better investment decisions. According to them, Altada’s solution has reduced the cost of processing a loan file by 90% while the technology has helped the firm to reduce the time it takes to process a loan portfolio from 6-8 weeks to just 48 hours. In complex, time-pressed scenarios where a decision is needed, this is Alpha stuff.
It is also timely. The European NPL ratio currently stands at 2.5% but is expected to grow to a peak in Q2 of 2023. In response, the ECB has urged financial institutions to get a handle on their credit risk situation – and fast. In a letter to CEOs, institutions have been urged to regularly assess borrowers’ “unlikeliness to pay” and enhance their existing processes accordingly.
As many have discovered, artificial intelligence is one way of enhancing those processes. The cutting-edge insights that AI technology delivers can help banks to identify an increase in credit risk, strengthening their hand as more performing loans are at risk of faltering into NPL territory.
Want to find out more? Book a free demo and we’ll show you how Altada’s AI solution can help financial partners to reduce processing costs, increase efficiency and optimize resource allocation.
Thanks for reading and please stay tuned for more blog insights!
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