Predictive models built using limited data sources don't always give a holistic view of the applicant. This can be challenging when assessing credit risk. Robust models benefit from a comprehensive set of variables brought together to generate a single or suite scores.
Data from open banking can be used in a standalone or hybrid predictive model that brings together multiple data sources.
Bank transaction data adds significant uplift to predictive models due to its comprehensive and dynamic nature.
Origination & onboarding
To offer personalised products and services at the onboarding stage, businesses need to understand their customer's financial circumstances.
Typically this process starts by requesting a PDF statement from the customer. Bank transactions are then manually categorised to understand how and where the customer spends their money. Affordability and financial health are calculated to assess the risk of onboarding the customer.
This process can be costly, time consuming, and provides a window for the customer to consider competitor offerings.
When a customer connects their account through open banking, businesses get access to all the information they need to understand a customer. Instant access to a customer's categorised bank statement removes manual processing from the equation, taking the process from hours to minutes.
Within seconds businesses can view insights on predictive cashflow, income, expenditure, debt commitments, affordability, and more, to make an efficient and informed decision.
Bank account verification
Businesses need to ensure that the people, merchants, or companies that they are paying are the correct ones.
In industries where fraud presents a high risk to revenue, verifying a customer's account ownership has never been more important.
Open banking provides the perfect solution. When a consumer or business connects their bank account you can be confident that they are the owner of that account.
Any concerns around third party fraud are removed with bank account verification. AML & KYC requirements are also satisfied.
Credit risk decisioning
Historically credit risk decisions have been made on self-declared information provided by the customer combined with information from credit reference agencies.
While decisions can be made, underwriters often lack the comprehensive, real-time view they need to make confident decisions.
Using open banking, decisions are made based on rich insights from accurate, detailed, and real-time data, that comes directly from a customer’s bank account. Advanced insights on income, expenditure, predictive cashflow, affordability, and more, give decision makers all the answers they need to make confident credit risk decisions.
Portfolio risk management
Risk managers need the most accurate data to keep their portfolios healthy and provide a personalised service to their customer.
Understanding the customer's buying behaviours, financial health, and unique requirements, helps drive the lifetime value of each customer and reduces churn.
Open banking data gives risk managers and marketers real-time, accurate data, direct from their customers. With access to data that uniquely represents each customer, risk managers can offer a personalised products at the time to drive lifetime value.
Collection & recoveries
Collections & recoveries teams need to understand their customer's repayment potential to make informed decisions on when and how much to collect. Agents need data that is real-time and accurate to successfully rehabilitate a customer or to optimise recoveries.
This process is manually intensive and often requires each customer to submit PDF statements and self-declare data. Agents then have to judge the customer's affordability.
With access to open banking data, collections teams are able to access a real-time view on their customers' finances. After a customer connects their account agents are presented with the customer's bank transactions, with the option to categorise. The data can then be used to assess the customer's repayment potential.
The process takes a fraction of the time, reducing subjective judgement, the reliance on self-declared data, and drives operational efficiencies.
Specialist credit & risk insights
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Ingka Investments makes EUR 9m investment in DirectID
February 22, 2023, Edinburgh, United Kingdom - DirectID, a UK-based, global fintech specializing in credit risk, risk analytics and predictive modelling, using bank transaction data, today announced that Ingka Investments, the investments arm of Ingka Group, has made a EUR 9m minority investment in the company.
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DirectID's Senior Data Scientist, Andy Spence, delves into the winding world of bank transaction categorisation and explains how we keep up with the ever-moving goalposts.
Managing your portfolio in January with categorised open banking data
DirectID’s Chief Commercial Officer, Clare McCaffery, explores how portfolio managers can use categorised bank transactions to better identify and prevent emerging financial distress in consumers.
James Varga appointed as Scottish Export Champion For the Department Of International Trade
In addition to James Varga being appointed as an Export Champion, his company DirectID has also been appointed as the new FinTech Champion for Scotland.
On demand | The future of account verification: In conversation with DirectID & Shieldpay
Join the Founders of DirectID & Shieldpay for a conversation on account verification and open banking on Wednesday 26th October.
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