How AI Decision Engines are revolutionising asset finance

Ernst and Young reported a success rate of 67% in predicting which customers could become dormant for a major bank by utilising AI algorithms. AI Decision Engines can even be used to recognise periods (or prerequisite events) when customers are likely to be contemplating buying an asset.

The vast majority of AI Decision Engines are used in credit decisions, risk and fraud processes.

Most tend to focus on key areas or are used in isolation of the broader customer journey. However, an AI Decision Engine can be utilised at any given point during the lifecycle of an asset finance transaction.

What is an AI Decision Engine and how does it work?

A Decision Engine is a rule-based system that is used to determine particular outcomes. At a simple level, it is a series of ‘IF’ conditions.

For example, IF Credit_Score = X, THEN do Y. When these rule engines are combined with AI capability, they can predict future customer and fraud outcomes through the analysis of historical datasets.

An AI Decision Engine does not seek to prove that machines know better, rather it codifies human experience into a set of rules that can be automatically executed.

It can also test that experience (and gut feeling) with an empirical approach by looking at historical data.

The key difference between automated credit decisions and manual ones is the speed of execution inherent in automated systems.

AI Decision Engines possess a codified learning and review loop, enabling them to learn as new data becomes available. It is important to note that human monitoring is essential throughout the process in order to train, monitor and clean datasets, and to resolve any ambiguity.

Building your own AI Decision Engine

In order to build the foundations for your AI Decision Engine, you must consider the wider data ecosystem that will make it successful.

There are three building blocks: Data & Governance; Rules & Policy; and AI & Learning.

Before you begin, there are a series of questions you need to answer to determine whether an AI Decision Engine is the right solution for you.

• Does the speed of credit decisions matter to your customer?
• Does your lending or leasing technology platform support automated credit decisions and AI capability?
• Do you have the capability to build your own bespoke Engine?
• Do you have access to software integrators?
• Do you have access to a Credit Reference Agency application programming interface (API)?
• Do you have historic customer or industry datasets to train your algorithms?

If your answers are predominantly yes, then you are ready to move forward. If not, you will need to consider how your business could acquire these capabilities internally or from external providers.

The models in the table below give a detailed insight into the building blocks of your AI Decision Engine strategy. The Starter model only requires initiatives that are absolutely necessary.

More advanced businesses will be aiming to move towards the Mature model. You can pick and mix requirements from the Starter and Mature models that best suit your business.

An initial pragmatic approach, and one we would suggest, would be to implement a standard automated Decision Engine (typically described as a ‘Scorecard’) that could later become AI-enabled.

Building blocks	Requirements and considerations	Starter model	Mature model
AI and learning	Buy v build strategy	YES	YES
	Lending and leasing platform (Decision Engine) capability analysis	YES	YES
	Analysis of available cloud AI Decision Engine solutions	YES	YES
	Feedback loop design	YES	YES
	Algorithm training	YES	YES
Rules and policy engine	Business specific rules:
	Portfolio rules and limits	YES	YES
	Funder covenants and policy	YES	YES
	Internal business strategy	NO	YES
	Manual intervention checkpoints	YES	YES
	Underwriting rules	YES	YES
	Credit scorecard	YES	YES
	Risk rules	YES	YES
	Fraud signals	NO	YES
	Asset security	YES	YES
	End-of-life rules	YES	YES
	Generic rules:
	Accounting and VAT rules	YES	YES
	Industry rules and models	YES	YES
	Regulatory requirements	YES	YES
Data and governance	GDPR	YES	YES
	Data stewardship/governance:
	Architecture	YES	YES
	Data asset classification	YES	YES
	Data reliability	YES	YES
	Data security, data ethics and permissions	YES	YES
	Data sources:
	Customer data	YES	YES
	Open Banking and PSD2	NO	YES
	Customer business/credit performance	YES	YES
	Asset supplier data	NO	YES
	Asset registration and ownership data	YES	YES
	Internal business data:
	Customer outcomes	YES	YES
	Customer behaviour	NO	YES
	Profit & revenue data	NO	YES
	Asset performance, depreciation and usage	NO	YES
	Credit reference agency:
	Credit and risk	YES	YES
	KYC/AML	YES	YES
	Fraud	YES	YES
	Adverse media/data	YES	YES
	Macroeconomic data:
	Interest rates	YES	YES
	Economic and industry performance	NO	YES
	Digital/omnichannel marketing data:
	Marketing optimisation	NO	YES
	Customer journey	NO	YES
	Customer behaviour	NO	YES

Source: Downswood

How AI can protect market share for asset finance companies

Asset finance companies that do not exploit the capabilities of AI Decision Engines run the risk of losing business because an AI-powered competitor will be able to make credit decisions more quickly.

Leigh Baker, co-founder of start-up EquipmentConnect, commented that the asset finance space is “nowhere near as advanced as in the retail lending space, probably about five to 10 years behind the curve”.

“This,” he says, “was driven by the lack of real-time data on SMEs, as they only have to file accounts annually (if incorporated). Now, though, with the advent of Open Banking and real-time management accounts extractors, this is changing.”

AI-powered asset finance providers will also be able to plug into the next generation of fintech credit aggregators that require indicative decisions in real-time.

In the case of the fintechs, these indicative decisions are fed into their credit comparison websites via an automated feed (API).

Commercial customers looking for a standard loan using online aggregators could discover products that they were not aware of, such as asset finance.

Asset finance companies without the technology to make real-time decisions will be invisible to potential clients using these online aggregators.

Therefore, asset finance businesses need to harness the power of AI Decision Engines. It is the only way they will ensure that they are ahead of the curve – and the competition.

* Downswood was founded by Carl Davis, a former chief technology officer, who has led many award-winning projects in the financial services sector. Projects include the digitisation of asset finance and invoice discounting and delivering data-driven automation. Davis holds an MBA degree from the University of Cambridge. Downswood helps clients realise their digital potential through strategic technology consultancy, technology procurement, systems integration and software development.