14 Jun 2022John Macdonald

How Artificial Intelligence Has Become Crucial in Managing Credit Risk

Credit risk is present in every financial transaction.

The longer the period over which financial obligations remain, the greater the variability in risk. By its very nature, real-estate is a long-term asset, and so it is vital to assess credit risk both at the commencement of the transaction and as it evolves over time. By its very nature, real-estate is a long-term asset, and so it is vital to assess credit risk both at the commencement of the transaction and as it evolves over time.

What is Credit Risk?

With the consequences of COVID-19, global supply chain disruptions, and ongoing climate-related concerns still unfolding, the assessment and monitoring of credit risk is becoming increasingly challenging.

In secured real-estate lending, there are three key factors in the analysis of credit risk:

  • Income stream: what income is available to service the interest on loan? This is assessed by considering both the contracted rent, as well as how it compares to the market rent if a replacement tenant was needed
  • Residual value: what is the value of the property to support the repayment, either through sale or refinancing on maturity of the original loan?
  • Security: the lenders ability to access the income or sales proceeds if the borrower becomes uncooperative

The ever-growing unpredictability of the modern marketplace makes already difficult decisions for real estate even harder. But with the utilisation of accurate data combined with AI analytical tools, the risk to large-scale portfolios can be minimised.

Why is Credit Risk so Difficult to Manage?

Generally speaking, lenders will enter transactions with an information disadvantage compared to that of the borrower.

Initially, the borrower likely knows the property intimately, while the lender relies on 3rd party information sources such as general market data, expert reports, internal data, and credit agency data. Consequently, this information asymmetry will be even larger during the loan term, as the lender is usually reliant on the information that is provided by the borrower. In some cases, this can result in lenders only finding out a building has been vacated once the existing tenant has left. In cases like these, early intervention is key, but only possible with access to reliable data.

Using Data and AI to Assess Credit Risk

A larger pool of both open source and proprietary data, combined with advancements in artificial intelligence (AI) technology, has opened up new avenues through which lenders can monitor their credit risk despite operating in a rapidly changing economic environment.

For instance, we have developed the program DataScout, which allows access to multiple data sources on a single platform. This enables lenders to create multi-faceted digital profiles both at initiation, as well as during the term of the loan.

By utilising DataScout, lenders can:

  • Improve their access to accurate data that incorporates a multitude of traditional and non-traditional factors impacting credit risk.
  • Access real-time data that allows for the effective monitoring of credit risk and enables early intervention.
  • Receive up-to-date data analysis and alerts regarding credit risk factors that change over the life of the loan.
  • Flag ways in which individual loans relate to a lender’s wider portfolio.
  • Develop and monitor credit risk algorithms and models that identify risk triggers tied to vacancy rates, economic data, and rental levels in equivalent real estate.

A Changing Reality

The demand for robust credit risk solutions has never been greater, but without access to accurate, real-time data, lenders leave themselves vulnerable to significant losses in revenue. However, by working in collaboration with programs like DataScout, lenders can operate with confidence in their decision-making from the point of commencement through to enforcement.