How AI and data-driven decision-making can help banking

AI-driven business monitoring could have prevented expensive glitch for Santander Bank

As most people were preparing to celebrate the new year, the UK’s Santander Bank was dealing with a crisis. On Christmas day, roughly 75,000 people who received payments from companies with accounts at Santander Bank received a duplicate payment transaction.

The total damage amounted to £130m, and recovery in these situations is a painful process for both the bank and its customers. Making things even more complicated is that many of those who received the erroneous funds are customers of different banks. It’s a big mess, but could it have been prevented?

Preventing revenue critical incidents with AI analytics

For banks striving to keep up with the ongoing shift to a digital financial world, one of the best investments they can make is in artificial intelligence — and the reasons add up quickly. In fact, a McKinsey report from 2020 estimates that AI can deliver $1trillion in additional value to banks each year. But, as the report notes, many banks have struggled to fully implement AI, held back by a mix of outdated operating models, insufficient investments in new technology and the lack of a clear AI strategy.

The right monitoring approach could have prevented or at the very least mitigated the incident at Santander Bank. It appears that most of the transactions happened in a short time, on the same day and perhaps in the same hour. Even if the bank was monitoring this type of use case, manual processes and traditional monitoring don’t cope well when disasters quickly progress. AI models are far more adept at catching anomalies in real-time and alerting intervention teams before the damage gets out of hand.

To respond to costly anomalies in real-time, organizations need systems that thoroughly understand the expected behavior of all components and transactions. Integrating machine learning and AI-empowered monitoring tools can aid this process by establishing a clear baseline of anticipated behavior across any business metric. Things like seasonality, customer behavior, and routine transactions help these monitoring solutions identify anomalies as soon as they occur.

None of this is to say that it’s a simple problem to solve. On the contrary, modern banking systems are incredibly complex, with integrated systems and transactions fragmented into multiple streams and sophisticated interactions with external partners (and competitors). Human observation of traditional dashboards can’t keep up with all this complexity.

Time to Abandon Traditional Processes

If banks and fintech companies need a reason to adopt AI—in addition to its benefits—they need only look at their current processes in relation to the fast-moving changes in the financial and cybersecurity worlds. Manual monitoring and lag times in finding and fixing payment errors just don’t cut it anymore. Customers expect real-time services. Cybercriminals employ sophisticated, machine-driven practices that move too fast for the human eye. Without AI-assisted automation, organisations can’t streamline operations well enough to keep up.

Online banking companies making use of AI have upped the ante on processing loan applications quickly. Established banks are employing AI to improve their existing loan-writing methods, which move at a snail’s pace by comparison and often deliver an incomplete risk assessment. In addition to improving the speed and accuracy of loan applications, AI provides a host of other benefits, such as pulling information in real time from multiple billing systems to quickly reconciling any failures to charge for services—yet another way it helps increase revenues.

We are in a digital era — digital meaning automation — and automation has to be controlled and managed by AI. Banks need to operate in real-time –whether they are delivering customer service or monitoring the security landscape. In short, they need to make AI a fundamental component of their strategic plans, involving everyone from top leaders to rank-and-file workers.

The potential payoffs of implementing AI effectively are promising. Replacing manual monitoring with real-time monitoring and analysis streamlines operations, improves efficiency and delivers better customer service. And not only can AI cut costs by automating processes and reducing errors, it can help produce new revenue streams by enabling a more personalised service, which also reduces customer churn. It’s essential to staying competitive in a changing environment, as well as for creating new opportunities to grow the business.

6 Best Practices

Adopting AI isn’t just deploying a new technology; it represents a cultural change in how fintech companies operate. It starts at the top with a focus on business, not just technology, and must become familiar and intuitive for all employees, not just IT teams. Following essential best practices will help fuse AI into an organisation’s business strategy. For example:

  • Incorporate investments in AI into the organisation’s strategic plans, rather than treating it separately as part of the IT budget.
  • Ensure that senior management and the board are fully aware of the importance of how AI and other technology will be used in the business.
  • Board members and C-level leaders must understand exactly how AI can reduce costs and increase revenues.
  • The organisation’s Chief Data Scientist should carry the message clearly, explaining to the C-suite what AI can and cannot do.
  • Understand the risks of this powerful technology. Senior leadership should be aware of the ethical implications of using AI in the business, and the kinds of dilemmas that have affected other sectors.
  • The combination of business goals and technology works both ways, so be sure the AI team has a grasp of business objectives and priorities, such as marketing goals or reducing bottlenecks in payment processing.

Source

About Vesolv

Vesolv uses artificial intelligence for autonomous, real-time analytics on all types of data across the enterprise. Unlike the manual limitations of traditional business intelligence, we give analysts control over their business with artificial intelligence solutions, to eliminate blind spots, prevent incidents and investigate root causes.

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