Embracing Technology Innovations in Financial Services

Niraj Vaidya
Consulting Sales Director, Oracle Financial Services

As banks and financial institutions continue to focus on margin improvement and finding new ways to generate revenue streams innovation using AI and machine learning are on the top of the list for many. The push to gain a competitive edge in the the digital market place is driven by the generational shift that is offered by cloud technology.

In the last decade, the shift to the cloud has brought with it seminal developments in enterprise technology. The next decade will see more significant change. With the increased adoption of cloud technology, most of the world of enterprise information technology plugs into a different universe. The universe that is built with services, tools, APIs, application development frameworks, and ready to access networks - all within a secure environment. Importantly, all of these can be fueled with elastic compute power and built out with generationally advanced and efficient technology at scale.

While this approach continues to gain momentum – banks are also learning a valuable lesson: these aren’t one strategy fits all implementations. CIOs have a tough task to formulate the right approach to deliver this to the businesses.

What is needed is a well-calibrated, but re-imagined approach is the key. The banks that are successful in this new universe are doing a few things consistently right and there are some fundamental tenets that are helping them achieve their success.

1. Open Up to an Ecosystem of Ideas and Collaboration

Banks take a very different path from technology companies when it comes to innovation, with stability generally considered more important than innovation. Collaborating with other players in the market has been a rarity. However, FinTechs and the challenger banks, along with technology companies are showing how innovation can be part of the key strategy for financial institutions based on:

  • Increasing cloud adoption in the enterprise computing space, deploying open-source platforms, building new integration capabilities across the platform, and standardization of APIs.
  • The rise of the FinTech community and opportunities for business-to-business collaboration.
  • Continued investments in technology products to help drive the adoption of new capabilities. Applications such as Oracle FLEXCUBE Universal Banking provides in-built adapters for Machine Learning and Blockchain.
  • Leveraging the technology industry investments in machine learning and AI. Technology companies are making their software open-source and available to a large community of developers across the network. Google’s TensorFlow, Facebook’s open-sourced platform based on PyTorch and Caffe2, Microsoft’s Infer.NET machine learning framework. Oracle’s Oracle AI Platform is an Open Source with libraries that are a part of the Python ecosystem. These Open-source frameworks for machine learning are a win-win ecosystem and are fueling open innovation, and everybody can participate.

Banks need to start to accelerate their open innovation strategy. The possibilities are enormous. Consider a digital banking application built with 1500+ RESTful APIs, fully integrated with a robust back-end core that uses machine learning adaptors in a secure environment to plug into a platform such as R by Oracle for your developers. It opens up a vast amount of room for the bank’s teams to direct more of their energies towards innovation and to solve problems creatively.

2. Focus on Tangible Use Cases

Getting the foundational technology in place and getting the technology skills onboard is the necessary muscle power for machine learning and AI. It’s the first step. Applying these technologies to the most profitable use case is the next.

There are hundreds of use cases that banks and FinTechs are experimenting with, for machine learning and AI. Of 12,000 fintech companies world-wide, nearly 3300 are focusing on machine learning and AI, and they represent close to a 20-billion-dollar investment in the fintech community as of today.

According to a leading analyst, if we plot a graph of the adoption rates and the maturity across two scales (x) “Tech maturity” and (y) “Business needs Maturity” - at least four use cases appear compelling.

Applications in AML, Compliance, and KYC

  • Banks are paying hefty non-compliance fees. Up to $2 trillion is laundered every year- or five percent of global GDP, according to UN estimates. The use of AI in terms of deep learning and machine learning is fast becoming essential.
  • Canada’s Scotiabank and Italian banking group Intesa Sanpaolo were able to process hundreds of data points (rather than just the usual 20 or 30 transaction categories) using this partnership with Ayasdi. (a FinTech using extensive AI for AML).

Location Sharing

  • Bancolombia’s Plink platform combines the Colombian bank’s vast store of customer data with analytics and AI to create value for both merchants and retail customers of the bank. Location information of the merchants put in context against purchase transactions, the timing, and quantity of purchase by customers and the rest of the spending data by the customers in Bancolumbia’s 10 million customer database is the target of this use case. Using ML algorithms, the platform realizes the value in terms of insights and actionable intelligence. This has already become a new revenue stream.

Investments by Robo Advisors to Personal Finance Management (PFM)

  • Over half of hedge funds are currently using AI or machine learning technology to inform investment decisions and generate trading ideas, according to a survey. It is still in an ‘assisted by AI’ mode, but very significant.
  • Italy’s bank Intesa Sanpaolo, Royal bank of Canada (RBC) and S. Bancorp, has partnered with fintech called Personetics’, a part of Oracle Fintech Network for its PFM app that uses machine learning algorithms. This offers a unique ‘goal-based’ savings product to its customers that automatically set aside money based on users’ spending behavior and predictive analysis for a targeted goal for savings. Similarly, BBVA has partnered with Google.

Conversational AI and Digital Assistants

  • CapitalOne bank in the US - created a buzz by offering its customers the ability to engage with Amazon Alexa for its banking transactions. The customer can ask, ‘Alexa, please pay my entire credit card bill.’
  • Kasisto is a leading Fintech who offers a conversational AI platform, KAI, which banks can use to build their chatbots and virtual assistants. It’s rooted in AI reasoning and natural-language processing, which can handle sophisticated questions about financial management. Several banks, including the UAE-based digital bank Liv., DBS Bank, Standard Chartered Bank, and TD, are using KAI as their backbone.
  • Oracle’s intelligent bots deployed on top of Oracle’s mobile cloud services easily the best in this league, which is so easy to implement and benefit from.

3. Think Creation Rather Than Disruption

Business as usual means that most traditional banks have the challenge of keeping the engine running making it difficult to get rid of the burden felt due to their aging infrastructure. This is the ‘technology debt’ in action. On the other hand, the barrier for a bank to become a super tech-savvy company is lower than ever. Slowly banks are starting understand these economics and opportunities and some have loosened up to the reality of today’s world. Many of them are increasingly employing more programmers, designers, CX experts, machine learning practitioners, and data scientists.

JP Morgan Chase is already operating as a technology business and invites communities of developers to its open-source projects on GitHub. The DNA of a traditional bank is gone.

Jibun bank - a mobile-only bank in Japan -with a scale of 1.9 million customers runs with less than 200 employees, and per employee, revenue is over 1 MM US$. This only indicates a high degree of ‘end-to-end digitalization’ that accomplished almost all of its customer journeys mapped and automated to the maximum extent.

The crucial factor amongst the winner is about maintaining a persistent and razor-sharp focus on adoption and innovation across every stripe in their banking activities.

To learn more about how AI can be leveraged for banking innovation, feel free to message me to explore more, or have a conversation.

For more information, please visit:
Oracle Machine Learning : Learn More
Oracle Machine Learning for R : Learn More
Oracle FLEXCUBE Machine Learning : Learn More and Additional Informaton
Oracle Retail Banking Solutions: Visit Solution Page
Oracle Financial Services: oracle.com/financial-services

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