How AI Decision Engines are revolutionising asset finance

davis carl

Asset finance providers are increasingly turning to artificial intelligence (AI) to assist with credit decisions.

AI Decision Engines are being used to predict which potential customers are likely to pass the company’s credit process, as well as to identify which leads are likely to convert into customers in the first place.

These Decision Engines could answer a key question for asset finance providers: are we getting customers from a particular marketing source that fail our credit process or result in suspected fraud? They can also help improve customer retention.

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

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
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.