The world of financial services is always a dynamic one, but banks today are undergoing a major shift. One of the impetuses to change has been the rise of digital challenger banks, sometimes called “neobanks,” which have increased fourfold from 60 in 2018 to 256 in early 2021. The power of digital technologies is also growing, and banks across the globe are eager to reinvent themselves and deliver world-leading customer experiences.
Banks have exceptionally high expectations for solutions powered by artificial intelligence to increase profitability and enhance competitive advantage, and for good reason. To cite just two examples: AI has reduced mortgage decision-making from two weeks to one hour, and AI helped a major US bank reimagine know your customer (KYC) processes, reducing cycle time and costs by 40%.
These are great applications of smart technologies that benefit customers and banks. However, achieving such dramatic results doesn’t come easy. It requires a holistic approach that challenges existing operating models and ways of working. It requires thorough integration of new digital technologies with existing legacy environments. And it requires the acceleration that only AI and machine learning can provide to mine the massive data sets that banks possess and generate insights that can lead the enterprise forward.
Mining for value - the hard way
While many banks are keen to start reaping the benefits of AI at scale, reimagining how processes can and should work is costly and complex. In many organizations, this remains essentially a manual exercise, conducted by consultants. Today’s banking consultant might feel somewhat akin to a gold miner from the 1800s, burrowing deep into the organization and its labyrinth of legacy systems looking for major transformation opportunities . Only then are they able to reimagine the organization’s processes and systems with an eye towards the application of digital technologies and automation while ensuring measurable business and customer value. The consultant’s journey must also take into account the evolving product and regulatory environment that is part and parcel of the financial services business. For banks, mining often feels like a never-ending cycle of discovery, documentation, and redesign.
This upfront human effort may be daunting to many in the financial services sector, especially firms saddled with decades-old legacy systems and processes. In fact, the effort is so intense that AI still has not yet created systemic changes in the financial services industry – even though it’s very clear that the time is right. Some experts say 70% of digital transformation initiatives fail because they lack the resources, expertise, and commitment that are needed.
Who hasn’t experienced the frustration of a customer service bot that feels like a barrier to expert help? Or a slick online application process that ends up requiring multiple emails and phone calls before the consumer gets the intended product? All too often, this rocky digital journey can lead to plenty of collateral damage, failed business cases, technical debt, burnt-out staff, and frustrated customers.
Mining for value: The digital way
Perhaps we can learn something from today’s gold miners. Most of the world’s easy-access gold has already been mined, and the cost of exploration often exceeds the value of discovery. Today’s mining companies are applying AI and machine learning techniques to massive geological data sets, essentially augmenting the capabilities of a trained geologist with the power of AI. This enables them to rapidly uncover hidden insights and focus exploration efforts to improve returns on investments.
Similarly, leading financial services organizations are beginning to test the potential of AI-based value-mining techniques to improve discovery and opportunity identification across vast data sets related to organizational workflows. For example, every step of a credit card application — from receipt and the credit report check all the way through to acceptance and card delivery — leaves a digital footprint. AI-based process mining software can ingest vast amounts of this data and rapidly deliver insights on what’s happening throughout the process, enabling consultants to answer questions such as:
The possibilities for insights and improvement are endless. In the hands of an experienced consultant, AI-based process mining can shine an extremely powerful light on what is happening operationally to help shape a bank’s transformation journey to benefit both customers and employees. Even better, automated digital value mining can uncover these insights in days, not weeks or months, enabling consultants to focus on the value-creating opportunities.
Wipro used this approach in a recent end-to-end review of payment processing for a major global bank. It enabled the team to rapidly quantify the impact of manual fraud checking on turnaround time and uncover best practice approaches across decentralized teams. This enabled us to reimagine an enterprise-wide approach using automation to reduce rework by 20% and fraud investigations by two days.
Using AI to mine data leads to a smarter, more holistic, data-driven approach to business reimagination that promotes the application of technologies like intelligent automation, AI, and advanced analytics to create dramatically different ways of working. By bringing together the best of talent and technology — in a well-sequenced manner, across departmental boundaries — banks will be ready to compete with digital challengers.
Next time you bring disparate teams together for a customer service request, ask them how well the processes are understood end to end and what reimagination opportunities have been found that transcend departmental boundaries. You might be surprised.
To learn more about Wipro’s AI-based data driven approach to value mining and reimagination, visit us or contact us at https://www.wipro.com/contact-wipro/.
Consulting Partner – Digital Consulting
Steve has 25+ years experience in shaping and leading large scale business transformation enabled by AI and Automation technologies including expert systems, analytics, conversational AI, voice biometrics and RPA. Steve is passionate about taking an outside-in customer centric approach to transformation that combines the best of human capabilities with AI (think Augmented Intelligence not just Artificial Intelligence), analytics and automation.