Fast tracking digital optimization in banking

As banks and credit unions around the world deal with the impact of COVID-19, many are realizing the imperative to make significant adjustments to their business operations.

With near-zero interest rates in most parts of the world, banks’ top line will be impacted significantly. With revenues impacted and labor costs flat, financial institutions will need to focus on cost-cutting through operations optimization. Meanwhile, with stay at home orders, customers’ demand for digital banking services only increases, as well as expectations for a fast, frictionless experience.

Many institutions, if they have been slow to adapt, now have little choice but to fast-track digital transformation efforts of the back end processes that support front end experiences.

But beware - results can be mixed when it’s part of a tactical approach, rather than a strategic, long-term vision. Some examples we’ve seen:

• Deploying bots to automate repetitive tasks, but with very little to show in terms of efficiency and effectiveness e.g. a top tier bank using bots to reduce the number of people supporting both internal and external IT support.
• Tackling more complex processes like KYC (know your customer) or trade finance but without a well thought out vision. This ends up automating only pieces of the puzzle and not the full process to realize the full value, For example, in the KYC workflow the meta-data extraction has been enabled, but the automation of more complex processes like UBO (ultimate beneficiary ownership) which deploy hundreds of people, has yet to be tackled.
• Many mid-tier banks have started the automation process only to find that the undertaking is more complex than the technical resources and budget allocated, leaving it half completed and without any clear gains.
• Some smaller/mid-tier banks have hired or reskilled developers in RPA/AI to quickly automate many of the manual processes but without fully understanding the workflow and domain, resulting in only incremental added value.

These early setbacks in the application of RPA (robotic process automation), AI (artificial intelligence), NPL (natural programming language) and other automation tools point to the need for a more holistic approach.

Next-generation automation

Technology is rapidly maturing, and domain expertise is developing among both banks and vendors so these early failures serve as key learnings in this new world of automation. A key use case in this matter is mortgage processing where automation has shown huge benefits. In another instance a large tier one bank introduced RPA across a range of processes - accounts receivable and fraudulent account closure - reducing its bad-debt provisions by approximately $225 million per annum.

Traditionally, banks have focused on using automation to cut costs, particularly automating back-office processes, advanced anomaly detection in transaction data for fraud detection purposes, and similar cost-saving measures. A truly digital company focuses on revenue and profit generation using next-generation automation tools.

We are slowly seeing automation with machine learning with structured data being widely used in trading and wealth management. With the emergence of machines that are learning to read and understand unstructured data combined with social media analytics, it offers the potential to do highly targeted product campaigns, dramatically increasing customer acquisition.

Another area leveraging AI/ML is in the front office in the form of Robo-advisors. First brought to market by Schwab it has now become a key differentiator in the wealth management space. Initial implementations are mostly matching individual risk profiles and needs with customized-model portfolios for a single horizon. As Robo-advisory becomes more sophisticated we will see it handle more complicated portfolios in multi-horizon windows.

A segment of one

Hyper-personalization, popularized by companies like Netflix to match a consumer’s interests with content, is also redefining customer expectations. Banks gather key customer data from multiple channels of interaction to build an accurate customer profile. Aided by big data and social media analytics, customers receive highly personalized offerings and recommendations.

Voice recognition is also playing a key role in making the user experience completely touchless with personal banking happening through Alexa, Siri and Google Home.

So we are at the point where we have the power of joining all the dots - with the help of AI-driven techniques, such as text analytics, Natural Language Processing (NLP) and Machine Learning (ML) on customer data –to enable a seamless, personalized experience.

Then comes virtual reality

Many banks have reduced their branch offices, replaced with ‘Virtual’ banks leveraging VR (virtual reality) and AR (augmented reality). A trading desk sometimes using up to six screens gets replaced by one VR-lens enabling the trader to look at even more data in real-time.

Blockchain has been a much-hyped technology but it is slowly gaining momentum. We are seeing blockchain in areas like clearing, derivatives settlement and payments. The technology is also being used by some large banks for client profiling and investment allocation, and another for all inter-bank payments.

Embracing digital optimization

Clearly automation plays a critical role in the digital optimization of banks – for increased productivity and dramatically enhanced customer experiences. More than 25 percent of work across bank functions will get automated resulting in increased capacity and better productivity. But, banks must take a strategic rather than a tactical approach to fully realize the potential.

By Sandeepan Mukherjee, Business and Strategy Principal, Growth and Solutions, BFSI, Persistent Systems