The nature of a bank’s business model means the origination of credit is paramount to generating revenue. However, a major aspect to lending is about the assessment of risk and that requires sound credit risk management. The efficient and prudent management of credit risk and the credit risk assessment process are critical.
A major element to credit risk management is establishing the Key Risk Indicators in order to understand the risk factors, loss exposure and risk mitigating factors.
With an emphasis on the potential of Open Banking (OB) in aiding responsible lending, the arrival of third party web-based OB APIs to help consumers and credit providers has resulted in new tools in credit risk indication and risk exposure mitigation.
Bank data, specifically, proves to be a reliable credit risk indicator for lenders to ascertain the creditworthiness of each customer when deciding on credit lending applications.
In order to use these tools efficiently and simplify the data sharing from customer to lender, automatic retrieval and summarisation of bank statements is one of the first benefits for the early adoption of OB in Australia.
Having the use of bank data means:
- Customers’ transactional data along with their credit scores will help lenders to make more accurate and responsible lending decisions
- Lending decisions such as credit limits and pricing can be personalised based on an analysis of a customer’s bank data to understand the debt servicing ability of the customer
- Bank data and credit scores can be assessed throughout the lifetime of a loan, which may provide advance warning of any changes to the potential risk exposure of a customer, enabling lenders and the customer to better manage their relationship
How digital data is changing the game
Bank data can indicate a consumer’s income and their expense behaviour, which gives lenders an improved picture of the consumer, allowing them to make more informed lending decisions and fulfil their responsible lending obligations.
Automated digital bank statement retrieval is simple and time efficient.
- Automated income and expense categorisation
- Bank data analysis and decision metrics to highlight specific risks
- A standardised format regardless of the institution
- XML / JSON formats for backend integration to increase automation
- State-of-the-art security with bank-level encryption
- More data from more sources
- No customer credential sharing required to verify customer supplied information
Lenders can categorise and group an individual’s income and spending behaviours which aid in identifying lending opportunities as well as flag any adverse data that may pose a risk to the lender and place the consumer in further financial difficulty.
Transaction categorisation capability allows risk managers to monitor:
- NZ Work and Income
- Work Cover
- Other credits
- Departments stores
- Dining out
- Pet care
- Subscription TV
- Sweep accounts
- Credit card payments
- Home improvements
- Uncategorised debts
Responsible lending flags can then be applied to the customer’s profile to assess risk:
- Debt consolidation
- Debt collection
- Debt management
- Overdrawn accounts
Using this data, lenders can:
- Identify the frequency of each source of income and expenses
- Combine categories to group into larger, more quantifiable data ranges that allow for a clearer view of an individual i.e. living expenses in the one group which include rent/ mortgage, groceries and utilities
- Ascertain metrics such as key dates an individual is in peak financial strength i.e. pay cycle and days where fixed expenses are taken from bank account
As more consumers engage with these new and improved data sharing systems, more lenders can approach their targeted clientele and offer accurate and responsible lending options, meaning a safer and diverse banking industry.
Step into Open Banking
Register your details now to download our introductory guide to the advent of Open Banking in Australia to discover what you need to know about the banking revolution.