The Credit Decision Engine

Apply your risk policies at speed

With the Trade Ledger platform, your teams can make lending decisions using up-to-the-minute data, assessed using controls that implement your risk policies.

In brief

The Trade Ledger Credit Decision Engine assesses the risks associated with business finance applications. It applies your risk policy and generates a credit risk score, providing guidance for business development managers (BDMs) and credit analysts (CAs) in making lending decisions.

BDMs and CAs can use the Credit Decision Engine to identify where an application could fall outside the risk policy, enabling them to make evidence-based decisions at speed. It reduces the time-to-decision for your applicants during the origination process, significantly improving the customer experience.

It offers much more subtlety than is possible with older methods of risk assessment, which tended to give binary options – if the application failed on one count, it was refused. By contrast, the Credit Decision Engine assesses the many criteria that are relevant to decision-making, enabling you to capitalise on a more nuanced approach – thus expanding the market you can address, or the products you can offer. It can support any credit product, including Asset Finance; we offer a ready-made template for Invoice Finance.

A risk policy is a key part of most lenders’ market offering, enabling them to appeal to their target market. Since the Trade Ledger Credit Decision Engine is configurable by users, you can easily adjust your policies to respond to market conditions.

Benefits

  • Get a rounded view of applicant risk
  • Improve compliance
  • Target a wider market segment without exposing your book to greater risk
  • Improve the customer experience with faster time-to-decision and time-to-cash
  • Get all the data and documents you need in a single place
  • See scores within the origination journey
  • Reduce costs thanks to greater accuracy
  • Use across multiple lending products

Risk Engines

Risk engines, such as the Trade Ledger Credit Decision Engine, use calculations and algorithms (computer instructions) to evaluate risk and provide analysis that supports decision-making.

Everyone is familiar with search engines such as Google: you enter a word or phrase, and the search engine assesses the information on web pages and advises you which are the most relevant.

The Credit Decision Engine assesses criteria from a credit application. It uses data from the applicant’s accounting data, application form responses, credit ratings and so on, and its output aids lending decisions.

How the Credit Decision Engine works

The Credit Decision Engine is easy to use, and is found on the Risk Assessment tab.

Risk Assessment screen from the Credit Decision Engine

The Credit Decision Engine assesses a range of the key risk areas considered in a credit decision. One example is sales ledger concentration for Invoice Finance. A highly concentrated ledger is risky, as it can mean the applicant is over-exposed to one debtor or sector or country. Other criteria include dilution and unusual transactions. Each can have a threshold, or control, that you set to reflect your risk appetite. You can adjust the configuration anytime. The Credit Decision Engine also checks applications as they’re being made on the Trade Ledger platform, and identifies risks from the answers to questions in the application form.

Debtor quality screen from Credit Decision Engine

When the Credit Decision Engine is run, it displays the results, together with an overall score, on a Report Card for the BDM, who decides whether to proceed to an offer and how to price it. It may also flag missing data or prompt for manual intervention.

Effortless

It’s effortless for the BDM/CA, who can price and approve a loan rapidly, significantly reducing the time-to-cash for borrowers.

The data that the Credit Decision Engine uses is already on the Trade Ledger platform – there’s nothing more to collect (except for the cases where manual intervention is required). Typically it takes a few seconds to a few minutes to run, depending on the quantity of data it needs to analyse, and it runs in the background so the results are generally available as soon as the BDM/CA is ready to review them – compared to 2 days with traditional processes based on documents and spreadsheets.

Configurable

The Credit Decision Engine allows a significant level of configurability. It can analyse multiple forms of data and address a wide range of criteria. It can be run repeatedly, if the application details change. You have the flexibility to assess risk in the way you want, that gives you market advantage.

The table shows some typical controls used to assess Invoice Finance facilities. BDMs can dig deeper into the data as necessary to aid their decision making. For more details about controls, just get in touch.

Typical controls for invoice finance

Questionnaire
assessment

The answers give a guide to sales suitability. The questionnaire might identify whether company A, which is applying for finance, sells to company B – but also buys from company B. This could make them higher risk and thus unsuitable for a facility.

Customer
quality

Automatically validates data points related to the borrowing entity, including financial health checks based on the balance sheet, profit and loss information, financial KPIs, creditor score, industry, and company size.


Dilution

Scores the observed dilution of the sales ledger – have credit notes or discounts been applied? What portion of invoices raised did the applicant collect? This indicates the headroom on the facility, and can be used to suggest the appropriate advance rate.

Debtor quality /
concentration

Shows debtors and concentrations, and flags any with concentration over your chosen level.

Debt
turn

Analyses the length of time taken for receivables to be paid. Does the debt turn exceed a configured threshold? Is there a deteriorating trend?

Debtor country
credit risk

Identifies debtors from high-risk countries and the portion of ledger outstanding to these debtors

Unusual
transactions

Shows unusual transactions, such as duplicate payments, and their dates.

The Trade Ledger platform

The Credit Decision Engine can make its near-instant checks because of the way the Trade Ledger platform works.

It’s a cloud lending platform that provides lending as a service (LaaS). It brings together all the data required to make lending decisions, from the application, the borrower’s accounting package, core banking systems, credit reference bureaux and supply chain systems. Everyone can work from a single version of the truth, enabling consistency and reducing re-keying and copy-and-paste errors. The data is available on demand for up-to-the-minute analysis and decision support.

The Credit Decision Engine draws on the data from the platform to arrive at its score, and is thus consistent with the rest of the origination process.

Supporting your digital transformation journey

The Trade Ledger Credit Decision Engine is highly flexible and can support your digital transformation journey at your pace. Talk with us if you are interested in:

  • Automated decisioning, to show an indicative position for borrowers
  • Exception management engine, offering event/exception-triggered alerts
  • Performance reporting – assessing how the borrower’s business is performing and recommending adjustments to their facility or fee structure
  • Credit policy management
  • Portfolio and product monitoring
  • Product-specific borrower recommendations

Bev’s Pear Company

Bev is a pear farmer who sells to big supermarket chains. Her payment terms are 30 days, but one supermarket is late in settling its bill.

Her cashflow isn’t great, so she asks her bank what funds she can access using invoice finance. The bank looks at her spread of debtors and finds that 50% of her invoices go to this one late-paying supermarket.

Traditionally the bank would have refused a facility if more than, say, 30% of Bev’s invoices went to a single supermarket customer. If the supermarket went bust, Bev might have defaulted, and recovering the funds would be costly and unpleasant for everyone. Bev might even lose the farm if she’d used that as security.

The bank could have done some more research – checking the supermarket invoices against Bev’s Pear Company’s entire ledger, calculating concentrations on a per-debtor basis, then going to a credit rating agency and buying a report on the supermarket’s creditworthiness and sending the evidence to a risk manager for approval. But why would they bother?

The Trade Ledger Credit Decision Engine eliminates this effort. The bank clicks on the Run Risk Assessment button and chooses the period over which they want to check the accounting data, say the past 6 or 12 months. The Credit Decision Engine assesses the invoices, and generates a scorecard showing how the applicant rates on a range of criteria or themes, with thresholds chosen by the bank, along with a final score. It shows, for example, which debtors have a concentration above 30%, what their concentration is, and their risk rating. Then it offers a credit check for each debtor.

With two clicks, the bank has risk guidance based on the latest data available. The process can also be fully automated.

In the case of Bev’s Pear Company, the late-paying supermarket has a strong credit profile, and so despite the higher risk the bank decides to go ahead with the facility. Bev gets her decision and can draw down on the loan within a few hours, thanks to the speed of origination available with the Trade Ledger platform.

Bev’s Pear Company continues trading, Bev can continue to employ her team, and the community she lives and works in continues to benefit. And the bank has expanded its loan book within its risk appetite.

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