Machine Learning, AI and the Future of Data Analytics in Banking

Banks and credit unions that don't embrace artificial intelligence and invest in the power of advanced data analytics are doomed.

Subscribe TodayTraditional retail banking providers, weighed down by monolithic legacy systems and ponderous regulations, are in uncomfortable territory. Advancements in fintech have upended the industry, enticing both large financial firms and smaller tech startups to apply disruptive technologies in ways that threaten the status quo.

To become more agile and remain relevant, traditional retail banking providers find themselves exploring their technological options with focused intensity. In particular, they’re looking for insights into customer behaviors.

The answer? Advanced data analytics.

New innovations in data analytics empower financial institutions with systems that are so smart, they learn on the go, automatically refining their algorithms and improving their results over time. This isn’t your grandpa’s approach to data analysis — spreadsheets, data tables and crunching numbers on a calculator. This is true artificial intelligence (AI).

Today, banks and credit unions can increase customer stickiness by having digital assistants effectively manage routine inquiries and provide personalized advice. All this can be achieved with minimal day-to-day oversight; it runs in the background, adheres to compliance protocols, and can dynamically adapt to new regulations.

Advances in automation and data-led intelligence put sophisticated AI technologies within reach of traditional institutions — those without the R&D skills and resources to pursue such initiatives internally. This is because the modern AI platform can essentially stand on the shoulders of the data- and process automation technology trends that preceded it. The data sets that capture the boundaries and basic interaction rules already exist and are within the regulatory purview.

( Read More: The Use of AI in Banking is Set to Explode )

Pursuing AI can be a complicated journey, with success hinging largely on each organization’s prior experiences integrating new technological innovations. How frequently do you tackle new IT projects? Is your organization comfortable with the process? How nimble are you? Can you keep your projects moving forward, or do they bog down with lengthy delays? After all, an AI initiative can encompass a vast array of digital components — from big data to cloud-based solutions. Banks and credit unions may want to adopt AI, but they must ask themselves… Are they really ready for it? Have they appropriately positioned themselves for success?

Key Fact: Half of all banking providers believe AI will have significant impact their business model.

According to the “Innovation in Retail Banking” report from Efma and Infosys Finacle, financial institutions understand the potential impact and benefits of AI, but that they are still hesitant to act. They are approaching it piecemeal, slowly building towards AI competency by stacking on more and more of the innovative technologies they know they will need — creating the foundation they need one building block at a time.

But there are other significant impediments to progress. In particular, the Efma/Finacle study found that half of banks listed their legacy systems as the biggest hurdle they face, followed by a lack of unified vision (44%) and a shortage of skills and experience (38%).

The Efma report found 58% of banking providers believe AI — along with several other technologies such as advanced analytics, big data and open APIs — will (eventually) have a significant impact on the industry. Noticeable progress is already evident in arease like automation, machine learning and data-led intelligence, which are already yielding new efficiencies. Still, AI will take several more years to reach its full potential.

While financial institutions estimate AI’s impact to be low in the immediate future — only 37% of respondents in another study by Infosys said they believe its impact will be significant in the next two years — the financial services industry is investing much more in AI technologies than other industries, and these investments will continue to grow steadily as banking providers get closer to achieving their fully-functioning AI-driven systems.

Ultimately, AI technologies will allow retail banking providers to focus on high-value activities and creative solutions around the customer experience. AI will eventually automate high-volume, repetitive tasks and at a lower cost. It will also be help banks and credit unions manage their regulatory and compliance burden, generating audit trails and flagging suspicious behaviors. It will even be capable of anticipating customer service issues and sales inquires.

These advances are being realized largely because many of the necessary ingredients for AI already exist. Regulatory and customer interactions are well documented — a repository of data that can be mined when creating automation algorithms. Big data tools can help parse and sort data for analysis. Machine learning technologies can help tease out the insights and context lurking behind your data, then establish predictive patterns.

All it takes is an AI solution to stitch it all together. And this is where many financial institutions are getting stuck.

That’s why three out of every four banking providers (74%) in the Efma study believe partnering with third parties will be the best way for them to access these exciting and innovative new technologies. Other options include funding internal R&D (46%) and working with partners from different industries (42%).

The Royal Bank of Canada is one example revealing how financial institutions are trying to wrap their arms around their AI and tech challenges. RBC is heavily investing in the University of Toronto’s Rotman School of Management’s Creative Destruction Lab — a lab that has nurtured some 50 artificial intelligence companies. The bank is going so far as to partner with the university to create its own research lab focused on artificial intelligence, the RBC Research in Machine Learning Center. AI depends on the advancements in technology and partnerships within the financial services industry and with technology firms. While barriers exist and banks may be slow to adopt to digital, AI, automation and other technologies are gaining momentum and have the potential to mold the industry in completely new ways.

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