Solutions / Data-driven loan offers

How machine learning can help you make data-driven offers to the right customers at the right time - and predict loan acceptance

Identify suitable targets for a loan offer

Boost the conversion rate of your campaigns

Make offers to customers with external loans

Enhance your risk scoring information

How can you improve the conversion rate of loan offers made to appropriate clients?


Knowing your clients well, and knowing when they need financial support is a key part of your success in lending.

But it can be challenging to know when your bank’s customers most need your help. How can you be sure you’re presenting them with the right offer at the right time? Which customers should you offer and which products should you offer them? And how do you reduce risk?

It’s not about financial services, it’s about key life events.

The key is to get to know your customers well – to accompany them on their journey so you can identify their needs. You want them to see you as a financial partner rather than a bank who is ‘selling’ them financial services.

And you want to make them an offer when they need it most, which is often after a key life event. We can give you the information you need to enable you to pinpoint those moments.

Our approach will help you answer the right questions

With the information we give you, you’ll know the answers to the following questions – helping you make the right offers to the right customers at the right time.

Which customers will be most open to accepting your loan offers?

Apply propensity-to-buy scoring to every single client.

When is it the right time to make the offer?

Receive prompt alerts in real-time when the opportunity to offer occurs.

How can you improve the conversion rate of your campaigns?

Give your call centre teams the tools they need to succeed, with data-driven contact lists and call scripts.

Who has taken out a loan product with another lender?

Identify clients taking loans with an external provider, with our instalment detection tool.

Does the client pass your credit scoring/risk assessment?

Use our unique social scoring to access additional information.

Our Solution

Propensity-to-buy model

Our solution enables the computation of the propensity-to-buy scoring for each client. We use complex machine learning algorithms to process transactional data and predict the buying behaviour of your customers, in combination with their social scoring.

Our model correctly predicts loan acceptance in 87% of cases

Instalment detector

Our instalment detection tool flags up customers who have taken out a loan with an external company. It suggests possible targets to approach with a better loan product, enabling you to help your customers and give them a more cost-effective option, while increasing wallet share.

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Our tool doubled the number of detected instalments, compared with the bank’s previous solution.

Data-driven campaigns

Enable your call centre teams to be more effective – by providing them with data-driven contact lists and call scripts. Our model is continuously learning, via feedback from current campaigns. The resulting data helps you generate the right leads automatically, and adjust the parameters as you go, to avoid inefficiencies and expensive lending campaign restarts.

Life-event monitoring

There are certain moments in life where your customers are most likely to require a loan. You don’t want to miss those short windows of opportunity. Our real-time monitoring system uses behavioural models to identify important events in your customer’s life. So you can be ready to help when they need it most.

Social scoring

Our unique approach to social scoring means we can use transactions to find similarities among customers, and reconstruct social networks of similar people. We use the strength of connections within certain social groups to determine the social score – and use it as a proven predictor for customer risk-assessment. So you know who to approach with your loan offers.

How it works

SOCIAL SCORING TRANSACTIONALDATA BEHAVIOURALMODEL SOCIALGROUPS SPENDING EMPLOYMENT HOUSEHOLD FRIENDS F AMI L Y INPUT T ARGETED L O AN OFFERS CLIENTDATA L O AN REFINANCING OPPORTUNITIESTO OFFER INPUT EVENTTRIGGERS INSTALMENTSDETECTION PROPENSITYTO-BUYMODELLING LIFE-EVENTSMONITORING D AT A-DRIVEN C AM P AIGNS HOUSEHOLD

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