“How’s the View?” And Other Questions for Our New VP of German Sales
Our new VP of Sales in Germany, Steffen Schreiber, comes to us with deep experience in payments technology, working with organizations like J.P. Morgan and Adyen. Read on to hear his thoughts on German banking trends, the advent of PSD2, and why Feedzai is the right place to be in the year 2017.
What are the particular challenges you’re seeing with payments in Germany?
PSD2 is top of mind in Germany as banks face new non-bank innovators entering the payments ecosystem. These new players pose threats to German banks, since PSD2 is giving innovators full access to customer bases that used to be exclusive to large banks. As our CEO wrote in the recent Paypers report on open banking, it’s an unprecedented playing field, leveled for a broad range of players, and the banks who ‘get’ digital transformation will be the banks that win.
Another area of focus for German banks is on seamless customer experience. This study from Roland Berger found that German banks are below average in service delivery speed when compared to other EU banks. This gap creates a competitive vulnerability because today’s customers, more than ever, demand immediacy. They’re used to instant, reliable, experiences, and they’re bringing these expectations to financial services. I want to guide German banks to focus on using machine learning to gain complete views of their customers, so they can turn those complete views into great experiences.
What I’m also seeing is that German banks have a cross-channel fraud problem. The majority of German banks reported losing information when switching a client between digital and physical channels. Any loss of information creates a void that fraud rushes in to fill. German banks need a solution that can consolidate data silos to build intelligence at speed and at scale.
What strategies are executives creating to solve these unique problems?
By now, it’s clear to most of us that machine learning is the answer. We’ve seen a shift in the question. Before it was: “Do I need machine learning?” Now it’s: “How do I bring machine learning in?” I want to help organizations connect this powerful new technology to their business so they can start experiencing the enormous value. But first we have some myths to dispel. For example, executives think that machine learning is going to replace their rules-based systems, when in fact, machine learning is part of a hybrid approach that also includes rules.
How is Feedzai tackling the current payment trends in a digital economy?
The payments world is changing at a dizzying rate, and that rate of change is only increasing. Banks are already adopting machine learning systems. AI is already woven into the fabric of payments across the world. How will you adapt to this inevitable change? Feedzai is using today’s most advanced machine learning technology to help banks navigate this change, and make commerce safe.
It is truly impressive the broad and deep impact that AI is having on banks. AI is now powering everything from back-end processes, like support networks, to front-end processes, like account opening. For example, a recent Accenture report showed that when one New York-based investment bank used machine learning to automate its IT environment, it reduced its average resolution and fix time by 93%.
Beyond automating the back office, AI is also augmenting every significant stage of the customer journey, which means that staff can increasingly focus efforts on adding value. The right machine learning system can break down data silos and bridge payment types and payment channels.
But the real game-changer in my opinion is how AI is able to support risk management while also supporting customer experience. Traditionally, mitigating risk has come at the cost of improving experience, but Feedzai’s advanced decision engine leverages the power of AI to support both goals with a unified approach.
Where are the business areas where machine learning can be the most useful?
As a powerful decision engine, machine learning technology can add value to almost every business sector of a bank. But I’d highlight two areas in particular where machine learning can influence very quickly. First, there’s big data. Financial institutions are data rich, and they have to manage this data in an omnichannel environment. They need to overcome poor data quality and overcome internal barriers. Machine learning can help. A machine learning system with omnichannel and omnidata capabilities lets it integrate ‘never before-seen data’ to create context and then make decisions based on that context.
By turning data into insight, machine learning provides the basis for the second area where machine learning makes a huge impact: customer experience. This is the leap banks have been waiting for, because they’re no longer different from consumer retailers and service companies. They need to engage customers with seamless experiences or lose them to competitors. So the focus is on account opening, because there’s an opportunity to create dynamic account opening experiences based on targeting and personalisation. Machine learning is the basis for building this new generation of tools. With machine learning at the core of a bank’s technology stack, what emerges is a 360 degree view of the customer, which means more application approvals and less manual overheads.
Why is machine learning a game changer for banking?
I think the number one thing is that it provides flexibility. The applications of artificial intelligence are endless. The AI researcher Andrew Ng has said that he can’t think of a single industry that will be unaffected by AI, except for maybe hairdressing. My experience has shown this to be true. The banks I talk to have large problems, and they’re all different. One bank might have a customer journey problem, with customers abandoning credit card applications. Another bank might have a fraud problem with transaction monitoring. Another bank might have a problem complying with PSD2 requirements when it comes to opening up APIs from the IT side. These are different problems, but they have something in common, which is that machine learning solves them all.
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