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While AI is the science and engineering of making computers behave in ways that normally require human intelligence, machine learning (seen by most as a subset of AI) is the study of computer algorithms that improve automatically through experience.

Some of the most fabled examples of AI and machine learning in history include:

• The defeat of six-time world chess champion Garry Kasparov by IBM’s Deep Blue in 1997
• AlphaGo, a program made by Google’s DeepMind, triumphing over 18-time Go world champion Lee Sedol in 2016, then losing to another version of itself in 100 consecutive games the following year
• The Facebook / Cambridge Analytica scandal

Examples of advanced AI in daily life include using your voice assistant built into your phone or smart speaker, performing a Google search, and auto-translating text into other languages. Meanwhile, driverless cars are becoming a reality, using image recognition continuously to analyse the environment and identify risks.

But this only scratches the surface of describing the role of AI in the world. And while DeepMind has successfully beaten humans at the most complex games, many people find the implications for healthcare, manufacturing, energy and financial services far more interesting.

We know that financial services companies are already seeing how AI tools can improve revenue, efficiency and risk management. IDC analysts suggest banks are expected to spend $5.6bn on AI solutions in 2019, and McKinsey predicts AI and machine learning could generate more than $250bn for the banking industry. But how is that reflected in asset finance?

There are some cases of AI and machine learning in use in asset finance, particularly around credit decisioning and fraud prevention, but the truth is our industry has always been somewhat risk-averse when it comes to the adoption of new technology. The caution of large-scale lenders often obliges them to wait for other markets to mature, so the use of the latest technologies like AI is not widespread in our world.

Nevertheless, as technology providers, we at Alfa have always pushed our clients to innovate. For example, we have helped many of our clients successfully deploy robotic process automation (RPA), particularly in the form of systemised workflow and business rules, in our enterprise software platform Alfa Systems. RPA is described by EY in its recent paper Robotics and intelligent automation: Combining the power of human and machine, as forming “a solid, enterprise-scale foundation on which AI can build”.

At Alfa this year, we are looking to move things forward with AI in asset finance - for both our clients and ourselves.

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At Alfa, we are actively investing in exciting possibilities where AI can deliver value for our clients, as well as in our own internal processes. Some of those possibilities are set out below.

For our clients:

• We are looking to develop a framework for identifying  relevant financial risks early and mitigating them. This will involve various flavours of automated, intelligent decision making and streamlining their processes.
• That framework would provide a client-facing API that will leverage the data and flexibility in the Alfa Systems platform - already found in workflow and business rules - to predict contract delinquency, respond to early terminations, conduct customer credit scoring and more.
• This would give our clients the ability to configure an automated response from within Alfa Systems. For example, depending on the severity of the risk, Alfa could create a case for human review, or send automated communications to the customer.

For ourselves:

• We are looking to use machine learning automation to improve our own processes, streamlining our software development lifecycle.
• We think AI will spot patterns in continuous integration failures, and diagnose potential programming problems as they are being written.

And for everyone:

• We are investigating how we can build a scalable, flexible, resilient platform, capable of being used internally and by our clients, which will pool large amounts of data for machine learning that is agnostic to both the source of data and the machine learning algorithm used to learn and predict outcomes. 

These are just some of the areas we’ll be working on this year. We’ll be publishing regular updates and a comprehensive report following our research.

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