Dr George Zarkadakis (below), Digital Lead at Willis Towers Watson, examines how AI will transform Financial Services companies into true digital businesses.
The world is changing because we humans change. We adapt to new technologies, but also adapt the use of those new technologies in our lives. As a result, our expectations and behavior change.
Say, for example, I am the customer of a bank. Nowadays it's not enough to know that I can trust the bank to keep my money safe. Now I want to be serviced according to my changing needs, almost instantaneously, possibly while on the move, while my personal data are kept safe. And here's the great challenge for banking, as well as for financial services in general: their customers have become hyper-agile, but the financial institutions themselves have not. Too much corporate inertia and too many complex, labor-intensive, back-end processes and legacy systems weigh down, and sometimes neutralize completely, attempts for true digital transformation. I say "true" because simply replacing a human in customer service with an app or a robo-advisor is not digital transformation; it's just a digital shopfront, and it's not enough. Financial services institutions should aim for an end-to-end transformation that begins with customer acquisition and ends with compliance and reporting, covering everything in between. And they need to do so quickly, in order to fend off innovative newcomers as well as more proactive competitors. The good news is that AI, and machine learning in particular, have come to their rescue.
The magic of machine-learning algorithms is all about making good use, and sense, of the massive data that financial services companies have at their disposal but feel they can do little about. So let's identify three major areas of inefficiency and see how AI can make the difference. Let's start with customer acquisition. The old days of shoot-in-the-dark marketing are long over, but AI offers even more advanced solutions in segmenting customers and engaging with them with more personalized and targeted offerings. Personalization is a key differentiator and unsupervised learning algorithms can deliver that competitive edge.
Operations can become more efficient quickly and with relatively small investments, by automating processes through robotic process automation (RPA). Vendors of RPA increasingly provide additional, cognitive automation functionality, such as natural language processing or image recognition. Think of an automated system for insurance that can process images of car accident damage and recommend fair pay-outs. The power of cognitive automation means that you can scale operations at a fraction of your current cost. And how about an AI system that can match skills with projects, and provide a talent platform within your organization, so that you can get the best out of your talent pool and your employees can have more satisfying careers?
Compliance is another area that can also benefit massively from AI. Think of risk data aggregation, modelling, scenario analysis and forecasting, fraud detection, credit risk detection, and identification of clients and legal persons. There are huge risks and inefficiencies to be found in compliance-related processes, which means that the area is ripe for intelligent automation. McKinsey found that regulatory fines and settlements in 20 large US and EU universal banks increased by 45x in the 2010-2014 period. According to Spanish bank BBVA, an estimate for financial institutions is now around 10-15% of total workforce dedicated to governance, risk management and compliance.
By analyzing processes across those three areas the leaders of financial services organizations can begin to strategize on how to become a true agile and digital business, with AI and machine learning as their engines of change and growth. A lean startup approach is highly recommended. Find where the low-hanging fruit is and develop a quick, proof-of-concept solution. And then scale it by applying sound change management and communication principles and practices. Cognitive computing, unlike previous technologies, is exponential, not linear. The future belongs to the organizations that are agile enough to catch this exponential wave and win.
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