Banking analytics implementation has come on in leaps and bounds in recent years. It wasn’t so long ago that it only extended as far as basic reporting and statistical modelling. But today, it can deliver financial intelligence at a granular level, supporting better compliance, stronger customer experiences and smarter risk management.
Despite this, only seven per cent of banks are fully leveraging financial services analytics for critical processes, meaning there is still scope for banking organizations to drive first-mover advantage in this area. In this blog, we’ll explore all the key use cases and how to practically implement a data-driven banking analytics strategy.
First of all, it’s important to understand the four different types of analytics that can be applied in a banking setting:
Analyzing and summarizing historical data to understand actions that have taken place in the past, achieved by aggregating data, finding trends and patterns, and visualizing them in ways that are easier to understand. In a banking environment, a good example of this would be looking at loan performance data to identify which products perform best and where improvements can be made.
This involves exploring data and patterns in great detail to understand why past events happened, through correlation analysis that can put underlying factors in the right context. For example, if there has been a surge in fraud cases, analyzing transaction data and customer profiles can help work out the source of the problem.
Predictive analytics in banking can be used to analyze historical data to predict trends and events that may happen in the future. This combines data mining, artificial intelligence and machine learning to identify patterns, and inform future decision-making with the help of data and detailed insight. This can be instrumental when looking at customer sentiment, risk management and market trends, as well as in detecting unusual spending patterns in real-time to expedite fraud investigations.
Prescriptive analytics takes the concept of predictive analytics to the next level: not only forecasting future events, but also coming up with recommendations for future actions. This combines predictive models with advanced algorithms that simulate various possibilities, and suggest the best actions to take to achieve desired outcomes. For example, this could be analyzing customer data to understand individual preferences, and generating personalized product offers based on those insights to drive revenue and growth.
When banking analytics implementation is done right, the benefits for banking organizations can be transformative. The level of insight that can be generated makes a real difference in five key areas:
The use of advanced analytics solutions can spot anomalous or suspicious transaction capabilities, far beyond the capabilities of even the most skilled human data experts. The ability to reduce financial losses to fraud, and to eliminate the wasted time caused by investigating false positives, is proven: HSBC’s AI-based Dynamic Risk Assessment system has reduced its false positive rate by as much as 60%.
Connected to the previous point, being able to proactively spot potential cases of fraud can combine with other functions to help reduce levels of risk to banking firms. This can include assessing credit risk of potential customers, and understanding likely future trends in consumer behavior.
This has become especially important at a time when ethical and responsible use of AI is in the spotlight: Capital One, for example, has applied a Model Risk Management framework to mitigate risks in this area.
Diving deeper into customer data, such as purchasing history, can inform more personalized and individualized customer experiences, including targeted product offers.
At Bank of America, for example, more than 35 million digital users have opted into a digital alert system, which covers information like debit card use and account balances. This can help build more trust and engagement among customers, who feel more valued and that their bank has their specific interests at heart.
With only 6% of banks able to deliver on hyper-personalization at present, this is likely to be a growth area for banking analytics in the months and years ahead.
Behind the scenes, analytics can help identify inefficiencies and suggest areas for improvement, which can help save time, money and effort. This can be achieved by addressing bottlenecks in processes, automating routine and repetitive tasks, and freeing up human resources to add value elsewhere.
For example, Lloyds Bank has deployed several new technologies to lighten the load on its workforce when processing documents. Optical character recognition, natural language processing and machine learning all help automate the extraction of key information for storage and processing.
Many key compliance tasks can be automated with the help of analytics tools, reducing the time and effort needed to meet regulatory demands and compile detailed reports. Automation can collect information from different systems and data points and quickly generate accurate reports. This can also play a part in supporting better quality assurance organization-wide.
Maximizing all these opportunities requires investment into some of the advanced technologies that makes in-depth banking analytics possible. This includes:
The ability to analyze and take informed action on financial insights in real-time, in order to improve customer experiences, strengthen security, optimize operations, and personalize customer experiences.
Initiatives to promote Open Banking and the use of APIs are fostering more interconnected banking ecosystems, which in turn opens up further opportunities for analytics. By navigating the complexities of Open Banking, data can be shared more widely between banks, FinTechs and third parties, enabling deeper insights and better service as a result.
As generative AI and other areas of technology continue to advance at pace, new innovations will continue to shape banking analytics strategy. In particular, blockchain technology will support secure, transparent and trustworthy data sharing across institutions; and edge computing will enable real-time analytics at the data source to reduce latency and speed up informed decision-making.
One of the reasons why take-up of advanced banking analytics remains relatively low is that a successful implementation is not the work of a moment. It needs careful planning, and expert support along the way - but if your organization gets it right, the rewards on offer are transformative.
From our expert standpoint helping organizations just like yours with banking analytics, we recommend the following to get on the right track.
Apply a range of frameworks and methodologies to evaluate your current analytics capabilities, and identify areas for improvement. These can include:
Evaluating current maturity and improvement opportunities across five areas - data, technology, people, processes, and governance
Assessing capabilities such as Data, Enterprise focus, Leadership, Targets, and Analysts to pinpoint where there are gaps in operations and strategy
Exploring data flows across critical banking functions to single out bottlenecks, redundancies and possibilities for analytics-driven efficiency
Even the best technology needs the right people behind it to make it happen.
Therefore, teams must have data analysts, engineers and scientists in place, who can respectively analyze and interpret data; build and maintain the required infrastructure; and deploy advanced analytical models. This should come alongside more general skills such as data management, data visualization and good statistical analysis.
These skills and roles can be difficult and expensive to procure directly, which is where the support of a third-party partner can be invaluable.
The only way to truly gauge the success of banking analytics is through accurately quantifying the return on investment being delivered. This can be achieved with a strong KPI framework covering the likes of customer acquisition costs, operational efficiency and risk reduction, overlaid against key financial and non-financial outcomes.
We find that many of our banking partners work best with a step-by-step banking analytics implementation roadmap in place, so that analytics can seamlessly integrate with the rest of their operations. We believe this roadmap should focus on four key areas:
Building this roadmap is a highly specific process that needs to be tailored to the individual needs of the business and its customer base. Ciklum has the expertise to help every step of the way, and the ability to design and implement your ideal banking analytics technology. Contact our team today to find out more, or explore our data analytics capabilities in more detail.