The only methodology that yields proven and measurable results is trial and error. No matter the MBAs in the room and the PowerPoint charts and simulations, nothing will compare to trial and error. If we accept this fact, and we should, then the thing to aim for is to maximize the amount of experiments in order to observe and measure what in fact works and yields the result we desire. Move fast and break things, so to speak.
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The only methodology that yields proven and measurable results is trial and error. No matter the MBAs in the room and the PowerPoint charts and simulations, nothing will compare to trial and error. If we accept this fact, and we should, then the thing to aim for is to maximize the amount of experiments in order to observe and measure what in fact works and yields the result we desire. Move fast and break things, so to speak.

How exactly do you do that, when you’re sitting inside a risk averse, global bank that manages trillions of dollars of transactions and funds? And can you ‘break things’ without ending up in an orange jumpsuit? In this post I’ll explore not only why that is possible in a complexly regulated, highly conservative and risk-averse organization, but rather why it’s critical for the survival and competitiveness of such an organization.

Tom Chi has this now famous talk he does from the early days of Google Glass. After hours of debate about the color of the heads up display (HUD), where the room full of smart, innovative people could not find common ground, a simple prototype was built in an hour yielding a simple result. Smart people argue about the color and with all the smarts, the prototype put an end to the discussion and formed an argument everyone agreed with. The thing with smart people is, they can make a self-serving, factually incorrect argument sound smart by their own vary nature.

Separating what you know and what you think you know.

Unless you are building on real data, you do not know anything. Real data has to be representative and it has to be valid, yet the very concept of rapid prototyping does not require it to be perfect from the start. It simply requires it to be experienced by someone who does not know, what the experiment is or have an interest in behaving in a predisposed way. Realizing that you are wrong is powerful. The way to be less wrong is to launch something and improve. You can only improve when you know how and where you are wrong. That’s important and arguably, that’s the quickest and shortest way to get to being incrementally less wrong over time.

A Word On Culture

Financial services and especially the largest institutions are risk averse and let’s face it, you want them to be risk averse. Yet all the reasons (excuses) why you couldn’t adopt a prototyping culture in lets say a bank, will fall away when examined closely.

Everything comes down to design. You don’t want to launch things publicly due to brand risk? OK – launch it internally to another division. Then launch it internally to a second, to a third. Then launch it to an early adopter group, e.g. a partner company. Build up your confidence in your process, design it so it can succeed. What about regulation and risk management? Build that into your process and even more so, into your products. Regulators and risk managers love nothing more than you giving them more granular data, more security and more sophistication. Today’s technology can do that efficiently. Give them a (self-serving) reason to support you.

Culture is important and it’s important to take seriously. Yet it’s crucial to ask questions. Don’t fall into the trap of assuming the way its been done before is correct, just because no one asked questions and even if they have asked questions, don’t assume today is no different than last year. Ask questions and question models, that’s the only way they really improve.

With Open Banking and PSD2 we are seeing a large scale cultural shift, toward technology-centricity. It is not always comfortable and large institutions are building their talent pools with cross-disciplinary technology competence. This shift is just beginning and the most successful early adopters we have seen so far in financial services, are those that have embraced it head on and made a clear commitment. Pulling Goldman Sachs into retail after 147 years of no retail operations and launching the no fee personal lender platform Marcus shows commitment and an organizational bravery tackling new challenges. Surely it was a calculated and tested modeling that led to the launch, after several pilots and prototypes internally, but in the market it looked quite smooth.

That’s the thing. Move fast and break things, but manage what you break and who’s looking. Financial services are moving toward an API ecosystem at a fast pace with catalysts like PSD2. Embracing the change requires a different thought process and models, but the rewards may be enormous.