OakNorth uses big data and machine learning to improve lending efficiency

OakNorth, a digital banking startup, is solving the problem of bespoke SME credit by combining credit skills with technology and data science – driven by its big data and machine learning platform, OakNorth Analytical Intelligence.
OakNorthOakNorth India operates out of Gurugram and Bengaluru, which are very critical to its operations. The Gurgaon office of OakNorth has more than 350 employees. Its Bangalore office has 150+ employees.

Sean Hunter, chief information officer at OakNorth, says the Covid-19 pandemic is bringing welcome change to the commercial lending sector.

When it comes to commercial lending, banks rely on risk models to make decisions. These models have been built up internally over decades of lending across thousands, if not tens of thousands of loans, but COVID-19 has exposed unexpected flaws in them.

Commercial lenders are emerging from COVID-19 by embracing digital transformation.

Historically, banks and commercial lenders have relied on a small number of monolithic suppliers and systems to provide them with broad capabilities, augmenting their own internal development, to provide all their infrastructure. These systems are patched to add features as banks grow and markets evolve. Mergers can lead to overlapping, incompatible systems; the bank’s infrastructure can make these systems brittle, costly and time-consuming to change.
Sean Hunter, chief information officer at OakNorth
Covid-19 and the subsequent government interventions, however, are forcing banks to move quickly: multi-year projects would never adequately address the emergency needs of customers and existential challenges of businesses. The crisis comes at a seminal moment for the industry, when many banks are beginning to experiment with cloud infrastructure. These solutions are able to provision (or decommission) infrastructure in seconds what previously would have taken years, and are well suited for rapid experimentation.

“Using ML techniques has enabled us for example to develop our Portfolio Diagnostics framework that integrates over 200 stress case scenarios with regional overlays. These are mapped to 1,600+ sub-sector groups that incorporate assumptions for impacts on key financial metrics such as revenue, operating costs, working capital and capex,” Sean Hunter said.

The Framework enables commercial lenders to re-underwrite loans and bring consistency to their credit approach through the crisis, running risk analysis on a consistent basis, as well as monitoring all credits more closely – given that sectors have become more volatile post-COVID-19.

Machine learning models such as competition identifier, clustering, driver analyser, sentiment analyser, etc. trained by OakNorth credit scientists draw upon millions of traditional (e.g. company data, industry and macro data) and alternative (geo-location, web-scraping, web traffic, consumer sentiment, point of sale, surveys) datapoints to overlay borrower specific idiosyncrasies into the credit analysis.

“This hybrid approach is a pragmatic compromise where computers perform various tasks to allow the analyst to be more efficient, but the analyst remains in the driving seat and is able to train the models and direct and shape the final outputs to ensure they are coherent and understandable. This also means that it is not necessary to solve the entire suite of problems before automation is of some help to overall efficiency, with analysts plugging gaps in the process that are not yet automated,” Sean Hunter said.

Analytical Intelligence is solving the problem of bespoke lending to SMEs globally.

Using AI techniques and models has enabled lenders to develop an understanding of their portfolios at the granular loan level and take into account the individuality of each business.

Instant Credit Analysis enables faster decision-making and consistent analysis across new credits, periodic reviews of existing cases, or detailed re-underwrites. For new potential borrowers OakNorth Credit Science Suite analyzes the business’ financial data, as well as dynamic data sets for sectors, geographies and macroeconomic trends, instantly delivering basic credit analysis on that business.

Instant Financial Analysis provides standardized models for each of the sub-sectors in our taxonomy, pre-configured stress-scenarios, automated vulnerability scoring and presentation of capital structure.

Real-time Sector Insights allows you to see industry insights that drive borrower performance tailored for over 100 sub-sectors.

Automated Peer Comparison uses machine learning to surface up to 20 peer companies to consider in your credit analysis, allowing you to make comparisons with the borrower.

Continuous Monitoring of Active Credits helps you turn monitoring into a real time process and lets you focus on relationships not admin. The Platform monitors billions of data points to detect anomalies in your loan book. This gives Relationship Managers all the information they need and more for check-ins or annual reviews with their customers.

Portfolio Diagnostics generates a forward-looking view with sub-ratings on Liquidity, Debt Capacity and Business Profitability using scenarios specific to the sector and country of the borrower.

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