Four marginal gains for the new era of credit decisioning

Businesses in the banking and finance sectors are increasingly looking to innovative technology to assist and guide their credit decisioning. The rise of Open Data means there are more data sources than ever before, whilst the evolution of Machine Learning has formed a key part of an analysis revolution providing the ability to capitalise on the increased volume of available data. Businesses looking to make the most of new technology can push their operations forward - however, getting the most out of software can sometimes mean making slight compounding adjustments rather than sweeping changes.

This advancement in technology comes at a pertinent time, as it's more crucial than ever to make rapid, accurate credit decisions to risk avoid losing valuable business. With rising interest rates and inflation, borrowers are under growing repayment pressure – the latest decision by the RBA to increase the cash rate target to 2.35 per cent means an average Australian household will be paying an extra $653 on their mortgage each month. At the same time, Experian research shows that 75% of Australian consumers expect home loan applications to be approved within three days, with more than half expecting a 24-hour turnaround.

Financial institutions of all shapes and sizes are leaning on technology and automation to assist their workflows, and market leaders are constantly finetuning their tools to make the most accurate and timely business decisions possible. In today's ultra-competitive marketplace, every marginal gain a business can make can helps them to go further. Here are four of the most effective.

Machine Learning improves efficiency

Machine Learning (ML) is part of the wider scope of Artificial Intelligence (AI) technology, and it is proving useful when implemented in credit decisioning processes. ML is able to increase efficiency in risk management, empowering lenders to make optimal decisions more quickly by enhancing predictive power for creditworthiness, affordability, fraud-based decisions and more. Lenders can provide better customer experiences and lower operational costs while increasing revenue thanks to the power of ML.

ML also provides the opportunity to realise the potential of a whole new pool of credit applicants. ML can comb through vast quantities of both structured and unstructured data in a short period of time, allowing people with limited credit history, or 'thin file' applicants, to access credit. Traditional techniques may have prevented these applicants from accessing credit due to their lack of credit history – but having access to other relevant data can mitigate the associated risks with approving a thin file applicant.

Continue honing strategy performance

Any business' approach to strategy performance should be iterative, not stagnant. Decisioning software that has assisted design capability allows lenders to continuously refine and improve strategy performance by recommendation modifications without introducing analytical resources. Faster testing and simulation of strategies can help uplift performance right across the lifecycle. Businesses can ingest data from both traditional and alternative sources, safe in the knowledge that this access to data is helping them to make better credit decisions.

Don't be afraid of automation – embrace it

Automation shouldn't be a dirty word or shied away from. Reducing and optimising the volume of manual reviews and lowering operational costs is the goal of any business, and automation can help to improve 'time to decision'. Use the latest models and optimised decision strategies to approve applications from qualified parties sooner, so they don't seek credit from a competitor. By improving responsiveness via implementing visual data mapping and transformation tools, businesses can reduce lead time to production by enhancing the way they ingest new data sources.

Lenders don't have to overhaul everything right away if not in a position to do so – instead, they can start automating by taking small steps. However, it is worthwhile for businesses to investigate how they can automate decisions that might otherwise require tedious and repetitive manual handling. Businesses benefit when they're able to reduce the amount of administration that is required for someone to submit a credit application.

Move decisioning to the cloud as soon as possible

Lenders that have moved to the cloud already understand the benefits – but many businesses are yet to commit to a Software as a Service (SaaS) approach to decisioning. While businesses may be waiting on core functions to modernise, those that move to the cloud find they can remain agile and lower their operational costs.

A SaaS approach to decisioning also ensures that businesses receive continuous upgrades rather than relying on internal staff to deploy upgrades on local systems. These updates can drive marginal gains that help a business stand out from the crowd, so modernising now means getting ahead in the near future.