How Modern Analytics are Improving the Annual Review Process

Reports show that 56% of financial services companies have implemented artificial intelligence (AI) technology in business domains such as risk management, so senior-level financial executives that haven’t already done this are missing a trick.

Jackson Hull, COO at OakNorth

Annual reviews are imperative. For commercial lenders, it allows them to stay on top of a borrower's credit risk, scope out any potential credit issues, and examine potential borrowers’ credit history.

Commercial lenders understand that extending credit comes down to their confidence in the borrower’s ability or intention to repay the loan. It’s comparable to visiting a physician for a check-up. It’s typically rare for them to see any red flags or discover any serious risks. And even if there are issues, things are usually fine if they’re caught early enough. The same can be said for annual reviews.

Since most accounts are healthy and benign, in essence, unnecessary time and attention is wasted. Not to mention, modern technology optimizes and routinely performs these credit check-ups.

Reports show that 56% of financial services companies have implemented artificial intelligence (AI) technology in business domains such as risk management, so senior-level financial executives that haven’t already done this are missing a trick.

In commercial lending specifically, particularly when it comes to annual reviews, an over-reliance on manual processes and human insight is holding portfolio managers back and eating up valuable time. By leveraging AI and credit intelligence, banks can transform the annual review from a time-intensive and inefficient process carried out once a year, to something active and constant, connecting analytics to action.

The problem with current approaches to annual reviews

A conversation we have time and again with commercial banks is about how frustrating the annual review process is.

Most borrowers are in good financial health, yet first-line credit teams are still expected to review their financials, determine their risk rating, and prepare the credit write-up. This takes almost as much time as performing a full credit assessment - time which could be spent originating new loans or working with borrowers clearly in financial distress and presenting the most risk to the bank.

This issue is compounded by the fact that a seemingly financially healthy borrower today could become stressed or default in the future. Still, the bank either has outdated data or not enough to discover this before a credit loss becomes inevitable. Despite sound initial due diligence and credit analysis, many things can happen internally within a business and externally within the macro-economic environment (climate change, inflation, pandemics, regulatory changes, etc.) which can impact its credit profile from one year to the next.

AI can speed up the annual review process and identify problems faster

AI built into credit intelligence, and portfolio insights can automate data analytics and help relationship managers, credit analysts, and portfolio managing teams identify potential credit issues before they reach a critical level. Instead of spending time analyzing each loan every year, they will have a real-time, 360-degree view of how each business in their commercial portfolio is performing.

Modern AI also enables a proactive approach to credit monitoring that minimizes losses by helping banks remain ahead of the curve and take corrective action before potential credit issues arise.

When paired with machine learning (ML) – a form of data analysis that automates analytical model building – AI creates opportunities to analyze data and group borrowers by suggested actions such as exercise caution, safeguard relationships, lending opportunities and more.

Subsector analysis removes the guesswork

AI solutions provide continuous underwriting automation and granular sub-sector data that allows lenders to re-underwrite their loan book based on multiple forward-looking scenarios within minutes. For example, a lender’s existing borrowing data from various bank systems can be analyzed and enriched with third-party data from thousands of traditional and alternative sources. Lenders can then undertake a forward-looking credit analysis and proactively monitor their loan book. It’s comparable to driving by looking in the rearview mirror versus driving by looking through the windshield illuminated by a high beam headlight.

Proactive monitoring provides a consistent format to view credit analysis and monitoring across different sectors. Lenders have the opportunity to set financial alerts, such as hard or soft covenant triggers, and the alert is fully explained using direct or derived data points as context, allowing the user to act based on their informed judgment.

At the granular loan level, AI solutions such as these can provide access to immediate and actionable intelligence to stress test loan books, proactively minimize credit losses and identify growth opportunities. Portfolio insights are also imperative when highlighting key metrics such as the vulnerability rating, maturing book sizes and loans.

Also, many banks — unfortunately — still rely on Excel spreadsheets, manually gathering data and human analysis. Software-as-a-Service (SaaS) solutions can help plug this gap by enhancing the risk management capabilities and increasing efficiency. Near real-time data and liquidity forecasts from various sources create an unprecedented view of the commercial loan book.

Scenario analysis and other forward analytics go beyond history

Modern banking must combine backward-looking data with a forward-looking view. This includes taking a route that adds granular, loan-by-loan data instead of a typical portfolio or sector-level approach to credit analysis.

Historical models of old will no longer suffice. Lenders should be using more specific granular data and modeling to enhance their decision-making. AI allows lenders to get ahead of industry-driven risk deterioration, including any financial issues or covenant breaches. All of this enables the monitoring of loans with the same tactics used in underwriting, but more efficiently.

Don’t fall behind

Modern analytics are merging the gaps and eliminating issues commercial lenders have found for decades in the annual review process. With the time lenders save on annual reviews, those precious hours and minutes can be allocated elsewhere, such as originating new loans or building deeper relationships with existing borrowers.

Now is the time to update your technology to alleviate the stresses and time taken on annual reviews. The future is now, and AI is the toast of modern technology for banks looking to boost their risk management. As the world moves forward — backed by technological advancements — now is not the time for commercial lenders to fall behind.

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