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Volume-to-Value: The Analytics Promise
By Ursula Cottone, Chief Data Officer, Citizens Bank [NYSE: CFG]
Analytics enables users to ensure that a customer has the right products and services. The buck doesn’t stop there, however. Analytics has also become a crucial tool in combating fraud and false claims in the banking sector. Banks are now smarter, backed by solid data-driven methods to ensure they minimize issues through their screening mechanisms.
The reason most organizations turn to analytics isn’t so much a lack of data as it is a lack of integrated data. The greatest opportunity for analytics in the banking sector may be the power it provides in terms of data quality. Banking institutions face the challenge of transforming disparate data storage units into an integrated processing machine that tells a story.
The Power of Data Science in Banks
Data analytics builds a picture for banks that helps them study workflow intricacies that may have otherwise gone unnoticed in a flood of reports, schedules, and processes. For example, an enterprise project management system for banks tells them about the status of a project, factors affecting its implementation, the progress since commencement, and its current stage. Marry this system with a powerful data analytics tool and you have a single-pane-of-glass-view of your projects and processes that facilitates quicker service delivery and project completion. Data analytics can also be crucial when it comes to knowing your team and studying their performances both individually and collectively.
On the external side, transformation of data into knowledge has long since helped banks understand their customers on a more personal level. Prior to the popularity of analytics, bespoke products that touched the customer on a personal engagement level were rare in the banking sector.
As such, analytics provides a dual-faceted tool for banks—one that reaches out to the customer on a personal engagement basis and the other that helps identify and mitigate fraudulent claims. Information security and technology are two areas in banking that heavily leverage the use of data, be it customer transactions or internal processes. This presents the unique challenge of providing bankers access to data to do various tasks but also—from a security standpoint—monitoring that access. Identifying probable risk elements and troubleshooting issues can be done at an accelerated pace.
Transactional pattern analysis is a common spin-off tool often used in fortifying the fraud mitigation performance of banks
No matter how turnkey the data analytics solution may seem on paper, the ship does not begin sailing without a good crew. Building in-house expertise and training the technology team must be at the top of the checklist for banks that are looking to research, employ, and deploy powerful data insight tools. A rather unconventional approach may be needed here—changing the way key stakeholders respond to major shifts away from legacy systems and moving into the world of big data and analytics. From a technology perspective, banks must make sure they have a complete understanding of their data infrastructure and the exact goals they wish to achieve by leveraging data. This will help them choose from a multitude of analytics tool vendors. It is equally important to forge the right kind of partnership with such vendors, a factor which will grow to define the ROI of the data solution.
Since this involves a long-term decision-making process, a “people’s perspective” is required. Developing leaders with not only the right technology acumen, but also business acumen, is crucial for banking institutions. Vendor partnership selection must be a technology discussion, as opposed to a business discussion, but it is important for the technologist to get up to speed on the business dialogues and connect to the bank’s commercial priorities. It all boils down to whether banking institutions have people who can support and harness the power of the analytical tools they wish to purchase, with a clear understanding of the achievables.
The Time for Artificial Intelligence
This requires the technology teams at banks to stay informed about trends in the market. The two main trends that are disrupting banking are artificial intelligence (AI) and process automation.
Smart wallets, stock advisory, wealth management, and customer relations are some of the exciting opportunities for AI to influence the banking sector. In fact, some banks have already incorporated AI and machine learning into their major pain points—fraud detection and stock monitoring. A smarter engine automatically paves the way for smarter and more refined processes that are automated to a high degree. The incentive for banks to adopt process automation lies in the redundancy of manual processing in crucial core banking operations. There has already been a major shift toward partnering with fintech providers that have already been doing the heavy lifting on these issues. With an intimidating quantity of data, the banking sector has great potential to create a better experience for its customers.