Across the banking sector, organisations are under pressure to innovate faster – but as customer expectations and market competition continue to rise, digital solutions are evolving at pace to help them stay ahead of the game.
Kuldip Chiheru, Head of Banking Platforms Strategy in Global Financial Services at Atos, trusted digital partner to a number of the world’s leading banks and insurers, explains the transformative benefits banks can reap by using AI and machine learning in the lending process.
When we look at the lending process, the use of AI and machine learning has huge potential to help banks deliver better service. Among other things, AI is helping businesses create a more personalised experience – from their products and services to customer relations, loan management services and credit default prediction.
The financial sector is already embracing this new technology: 56% of financial services companies use AI to make risk assessments and 52% use AI to generate revenue from new products and processes. However, institutions are not harnessing this technology in areas that aren’t so data intensive, areas such as loan pricing, compliance and managing customer relationships.
Therefore, while AI technology is widely available and 15% of venture funding for AI in banking goes towards lending solutions, banks are still in a phase of modest experimentation rather than strategic use of AI.
The Benefits of Embracing AI
When harnessed properly, AI can be a powerful tool in supporting decisions around credit provision, as it significantly speeds up the loan application process, increasing efficiency and satisfying customer expectations, as well as driving down administration costs.
Banking conduct regulators abide by an AI design requirement that algorithms are based on ethical and explainable logic. Consumers therefore benefit from personalised, transparent and consistent decisions, while banks benefit from enabling higher quality services that safeguards brand value.
The use of AI allows banks to move from static products that target broad demographic groups to highly personalised ones based on individual data footprints. Moreover, the richer and higher quality the data banks input to AI, the more precise their models and predictions become, which is key to businesses’ survival amongst a plethora of smaller and more agile banking start-ups and scale-ups.
How Banks Can Embrace AI
One of the crucial elements for successful adoption of AI in lending is to identify discrete areas of application and tackle them one at a time.
For example, machine learning can be used to analyse and predict customers’ financial habits and suggest loan needs and repayments structures, helping to improve customer acquisition.
This data can then be used to determine credit scores as part of a risk assessment of a new customer, by providing an overview of an individual’s digital footprint.
Once the credit decision is taken, AI can be used to minimise a credit default and ensure effective and efficient loan management.
Finally, by using machine learning, banks can analyse current risks to loans based on news and market developments, as well as predict the probability of a credit default using historical, comparative, demographic and market data.
The future of lending starts with AI
In the quest for highly agile operating models, AI can play a transformative role. The banking industry’s journey to fully embrace AI is well underway, but the technology remains hugely underused within the sector.
Assuming banks keep pace with this technology, the potential benefits of AI could be seen through reduced credit loss, higher revenue per loan, lower due-diligence costs, lower servicing costs and a better end-to-end lending cycle.
It’s time for banks to fully embrace the opportunities this technology offers in every step of the lending process. By doing so, they can offer hyper-personalised money lending products and services to meet customers’ needs, which can drive profitability and encourage brand loyalty at the same time.