The financial services industry has seen a great deal of disruption from digital-based alternatives. Many of these challengers use advanced technology and expanded data sets to offer apps that provide financial solutions at a lower cost, with less friction and greater personalization than traditional bank or credit union offerings. Toronto-based startup Flybits believes that the best way to compete in the future is not just by developing innovative products and services, but by becoming the repository of choice for data in addition to money.
“I definitely see that banks are in a perfect position, if they innovate right, to be the perfect data vaults for the future – managing the privacy and also the data of their customers,” says Hossein Rahnama, CEO and Co-Founder of Flybits, in an exclusive interview for Banking Transformed, a new podcast from Jim Marous and The Financial Brand. “Using AI and machine learning, there is the potential to build a ‘data marketplace’ for banks, fintechs and other data providers to partner and build more services together.”
Financial institutions must customize communications to consumers based on contextual cues — e.g., offering personalized messages on a mobile app based on recent transaction history, the mix of products held, the user’s calendar, and the location+current activity of the user. Integration of data and AI could also trigger digital concierge services, allowing access to expertise never before available – in real time and through the consumer’s preferred channel.
I interviewed Rahnama for insights into how banks and credit unions can better collect and utilize data, and how traditional financial institutions could be positioned as the trusted data repository of choice in the future. Here’s some of what he had to say.
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Tell me about the Flybits solution for the financial sector.
Rahnama: If you look at the consumer sector you’re seeing that digital channels are becoming more predictive, more learnable, and more context aware. Think about Siri and Apple. Think about Amazon and Alexa. Think about Google Assistant and Google Live on Google. These are next-generation digital services that can learn from their users, and can get better as their users use them.
In the banking world, almost all banks are trying to build such services on their digital channels – next-generation concierge services that can understand the needs of their users and can adapt and give the right information to the right user at the right time. That’s what we refer to as “context-aware computing” or “contextualization.”
Building these types of capabilities in the past required a lot of I.T. processes, algorithmic expertise, understanding things such as statistical modeling and predictive modeling. Flybits has really simplified that process for banking institutions.
Instead of expecting the institution to hire data scientists and algorithmic experts, we have built platforms that even a marketing intern can be trained on, allowing them to focus more on use cases and creativity rather than worrying about I.T. complexities. This allows the bank or credit union to bring these next-generation predictive use cases to the market faster and in more efficient ways.
So, you’re talking about proactive engagement?
Rahnama: In order to build a capable, scalable, AI strategy for banking you need to have a lot of data, and you need to have a very good data strategy around that data. Flybits empowers the bank to leverage their proprietary data assets – correlating it with external data assets – putting the data sets together in an effective way to build customer logic.
They can use that logic to do better automation within the bank. They can also use that logic to engage with their customers better. But the key thing we highlight here is that customer identifiable information is fully protected and in some cases the banks don’t even see the individual data. We encrypt the data and tokenize the data. And, although the data is not identifiable, the financial institution can still use the data logic to engage with their customers in a more personalized fashion.
Would this have an Open Banking application?
Rahnama: Our belief is that the banks of the future will not necessarily be seen as banks or finance institutions. They will be seen more and more as data hubs or ecosystems for a much broader set of industry verticals. Not only will they provide better financial products and more relevant financial products consumers, but they will be in a perfect position to leverage data to form data alliances and data partnerships with other members of the ecosystem, such as grocery stores, airlines, energy companies, etc.
And, it will be up to the user if they are willing to share their information or not. If they share, they will receive benefits and services from members of these alliances, which are complementary businesses that are sharing data based on the consent of the user to receive better, more relevant and higher impact service.
How is privacy addressed in this model?
Rahnama: We look at privacy not just from a data point of view, but also from a design point of view. The paradigm will shift from the institution having a lot of data that they can mine and push products, towards a model where the user is willing to share more and more data to get better service from the institution.
And, the moment they are unhappy about the service, or uncomfortable about how the data is being shared, they have the ability to de-link all of that data. In the future, you won’t need to co-locate data in one location, making it less prone to security and privacy risks. Instead of sharing individual data, there would be a trusted edge that all alliance members can rely on to understand the insights, patterns and correlations.
Is the banking industry in the best position to be the data vault of the future?
Rahnama: In order for an organization to be able to lead this, they need to have a lot of data. We all agree that many banks have a lot of data, especially when it comes to transactions, income level, credit score, etc. This is data that many tech giants such as Facebook and Amazon and Google do not have access to, and that is why they are trying to get into the financial sector.
The second attribute we need to have is a lot of existing users. Banks have a lot of users, and are more trusted than the big tech firms. I think they are also in a good position to leverage digital design, bringing creativity such as storytelling and narrative design together, becoming the hub of the next generation digital concierge services.
How do legacy financial institutions structure their data for real time accessibility?
Rahnama: Banks and credit unions need to gain a greater maturity on data science and the understanding of what data ecologies can mean in the banking sector. As part of banking’s digital transformation strategy, many organizations are integrating internal and external data to engage with their customers better.
Unfortunately, about 90% of their budget was usually spent on managing I.T. complexities and data complexities, with few use cases being brought to market. The good news is that, with the advent of micro services, and effective orchestration in the cloud, an organization can cluster the bank into smaller, more agile product units and empower them around product capabilities rather than function and feature capabilities.
Do you see financial institutions providing ‘data vaults’ in addition to ‘money vaults’?
Rahnama: In the past, you would go to your local branch to deposit a check. In the future, you may still go to your local branch, but you’re going to deposit your sensitive data. You won’t give up the data. The data will belong to you as the deposits were owned by you, but the bank now has the responsibility to manage that data on your behalf.
So, I definitely see that banking institutions are in a perfect position to play this role if they innovate right. They can be the perfect data vaults for managing the privacy and also the data of their customers on their behalf, and use that data to drive healthier behavior in their community.
There is a lot that can be done there. This could be around the notion of connected cities, smart cities, really leveraging these new types of shared economies to drive better behavior. To achieve this vision, banking will need to understand the value and importance of data in achieving such transformation. Not just on an immediate basis, but also for keeping the institution relevant for the decades to come.
What will be the role of voice in the future?
Rahnama: There are a number of interesting interaction layers that enable banks to interact with their customers better. These could be conversational interfaces, like we have seen with chatbots. There are also voice assistants, like Google Home and Alexa. These presentation layers allow analytics to understand which interaction model is more relevant for that customer in that particular situation.
For example, if you are in a busy environment, maybe the right way to interact with you is through a display-driven interface rather than voice. If you are in a position at home where there is not a lot of ambient noise, and your interaction with your bank or credit union is more passive rather than active, maybe switching to a voice assistant will be better.
But I don’t think we should compare these channels and think, “Which one is better than the other?” I think we should think about a contextual layer that determines the needs of our banking customer when they are at home and the needs of the same customer when they are traveling at an airport requiring cross-border travel services. We need to have a deeper look in terms of what infrastructure do we need to use the right channel for the right use.
What do you see as the leadership challenge in banking?
Rahnama: There are a number of factors that leaders in a bank should look at. One of them is organizational. Usually, if you look at the structure of the bank you have core silos. I think the first thing that banking leaders should do is to really create a horizontal and abstraction layer among these units. This is so they don’t compete with each other, but also so they complement each other.
One way to do that is to create smaller, more agile, more interdisciplinary units in an organization, rather than to say, “Oh we have an AI team of about 200 scientists.” That is the job of a university, it’s not the job of a bank. The interdisciplinary teams should have people from computer science, data science, design, business, project management, etc. – empowering them to bring capabilities to the market. There are technologies that allow this to be done in a more effective basis in a secure environment … like banks.
The second thing is for leadership to understand the value and applications of AI. Do you want to use machine learning to manage risk? Do you want to use machine learning to optimize selling and up-selling? Each of these require a different type of AI strategy, AI infrastructure and expertise.
Finally, it’s very important that the innovation arm of a banking institution is interconnected to a business unit and there is an understanding of how to solve a pain point. You need to connect the innovation unit of the bank to the leaders of business units, and create a compensation structure and incentives for these individuals to talk to each other. There is a need for organizational alignment between what business wants and what innovation can do.