It’s widely known that artificial intelligence (AI) and machine learning (ML) can’t run on empty. Rather, these types of tools require copious about of data in order to provide value to a business.
The more data, the better, (as long as it’s good quality) which is why financial institutions such as banks have so much potential when it comes to leveraging AI and ML tools—banks are often sitting on a virtually endless amount of real-time information, which can be interpreted, analysed for patterns, and ultimately, learnt from.
By adopting digital transformation and pursuing a dynamic and agile analytics-first approach, banks can utilise their abundance of real-time data to better identify risks, make better business decisions and future-proof their business.
Banks are operating in a very uncertain landscape, with emerging risks in climate, geopolitics and cybersecurity and on top of this are subject to increasing levels of regulatory scrutiny, leading to higher costs of compliance. All which require the ability to be agile and provide on-demand responses and decisions that are grounded in data analysis.
How is risk analytics helping the banking sector?
Many banks are yet to recognise analytics as an important and strategic pillar. There are three primary areas of risk in banking: credit, market, and operational risk, all of which require policies and a level of risk analysis.
When an individual applies for a loan or any kind of credit, a process called underwriting begins. This process often begins with an automated screening based on credit scores, payment history, and debt to income ratio.
However, other factors also go into calculating credit risk, such as geographical locations or the borrower’s occupation. As a result, the practice can be vulnerable to human bias – a danger that automated risk analysis mitigates against. Helping to create a system that is fairer and more efficient.
Risk analysis can also be harnessed to better manage and leverage a bank’s assets. For example, it can help to inform important decisions about what credit to pay off, what to maintain, and when to increase cash reserves rather than borrow.
However, as mentioned, banks are also subject to factors beyond their control, such as market variations. Unsurprisingly, the market has been exceedingly volatile over the last few years with the knock-on impacts of COVID-19 and other international factors like the Ukraine War. Market volatility is always a risk, it can be impacted by everything from interest rates to energy demands making it unpredictable and hard to navigate.
Risk analysis can help banks understand the potential impact of market and external forces, as well as make predictions, based on previous information and scenarios. This again can provide insights that will help to shape future decisions and better protect the business’ interests.
Operational risk is present in any business and is essentially the risk of loss resulting from inadequate or failed internal processes, people, and systems or from external events. But banks – namely due to the lure of financial gain and sensitive information, can be especially vulnerable to things like cyberattacks, theft, and fraud.
Many of these risks can be mitigated using approaches such as training and operational security, but risk analytics can go a step further, helping to banks to take proactive steps to pre-empt risks, rather than working reactively, after an incident has occurred. For example, risk analysis software can help by modelling certain scenarios that enable the development of preventative solutions before a problem occurs. This can enable banks to evaluate security risks, prepare for the worst-case scenario, and put response plans in place.
The likelihood of risk
Risk is at the forefront of the banking industry concerns, and predictive analytics can help banks to take some control of the uncontrollable via forecasting and establishing probabilities, predictive analytics can help with.
- Credit scoring – predictive analytics can enable better analysis of actual credit risk tomorrow rather than just historically.
- Fraud detection – early detection can lower the risk of fraud or prevent it altogether, revealing areas of weakness within systems and even individuals.
- Cross-selling – predictive analytics can tell employees who are likely to open new lines of credit or use other banking services, improving customer engagement and sales conversion rates.
A coordinated approach
Risk isn’t something that can be managed with one policy, instead it needs to be tackled holistically, and considered as part of a bank’s wider strategy. For example, any digital transformation journey, should include an integrated risk management strategy.
And although analytics will typically be the remit of a data team, managing risk, particularly in a bank, is the responsibility of all employees, and requires buy-in from every level. It is therefore crucial to encourage dialogue and collaboration between stakeholders across all parts of an organisation. This helps to break down the silos, which is essential when looking at interconnected risk factors.
Don’t forget the customer!
Whilst risk analytics in banking generally focuses on preventing against what could go wrong, analytical processes can also be useful in elevating the service offered. For example, senior management can gain valuable inputs at each stage in the customer lifecycle. Analytics can help to provide insights into the typical life cycle of a banking customer, as well as behavioural segmentation and differentiated pricing. All of which can help to enhance customer identification, onboarding, management, cross-selling and retention.
Looking to put your analytical skills to the test in a fast-paced commercial environment such as banking? Get in touch with the team today, who can match you with a role that excites and challenges you.