This article was originally written as part of a PDF report sponsored by Daitan, and was written, edited and published in alignment with our transparent Emerj sponsored content guidelines.
The article features use-cases from Daitan, but also highlights transferrable lessons for other business leaders in financial services.
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Today’s FinTechs and financial institutions (FIs) need cutting-edge technologies to navigate our digital-first world. AI-enhanced innovations promise disruptive change to financial sector companies ready to embrace the latest machine-learning and natural language processing technologies. But, to enter this brave new world, FIs need to reimagine the role of data in their operations and strategies.
Already, fintechs have begun to chip away at the slow melt of glacial change in the way FIs share information with their customers. Opening banking, pioneered by third-party personal financial management tools, has captured the attention of emerging fintechs. Through application programming interfaces (APIs), banking customers can now communicate with their financial institutions using the communication platforms that they choose.
The advent of new natural language processing (NLP) technologies enables FIs to analyze customer communications and derive insights about how best to drive engagement. Algorithms informed by machine-learning empower financial institutions to determine when and how to best reach their customers, and how to structure and create their communications with them.
Fintechs and financial institutions today need data to construct holistic, data-driven strategies that foster demand generation and better customer experiences.
Those who can embrace AI in their customer engagement and experience strategies look to seize upon key benefits in their digital-first approach:
- New markets of digital-first consumers
- Lower customer acquisition costs
- Increased use of, and loyalty to, purchased products
- More cross-selling opportunities
AI helps fintechs and FIs navigate and use data, but what about compliance? In a highly regulated industry like financial services, data privacy and loss prevention share center stage with any other strategic initiative. But, that does not mean FIs cannot rely on data to drive their decisions and strategies. With the right tools, FIs too can make data-driven decisions and form data-driven strategies. They can reach their customers when they want, where they want, and how they want, if they form the right partnerships and develop the right strategies and products.
When you deliver what customers want, they come back with their business. A 2020 McKinsey Report found that deposits grew 84% faster in banks with the highest customer satisfaction ratings. The rewards for financial institutions that successfully navigate the advent of AI and its innovations could not be more promising.
Use Case 1: Open Banking
More than half (53%) of today’s millennials prefer texts over voice phone calls, according to 2017 OpenMarket research. Even more, 59%, say they receive account activity, payment reminders, and fraud alerts through text messages.
The banking industry is racing to keep up. To win the business of today’s increasingly influential younger generations, FIs look to shed their staid, traditionalist reputations and adopt open banking approaches for today’s customers.
To bring greater financial and communication transparency to their account holders, open banking technologies promise to expand communication platforms beyond phone calls and emails. Through open APIs, FIs can discover new ways to bridge the gap between today’s communication platforms and their own applications and services.
The challenge in open access, however, lies in FIs’ abilities to comply with prevailing regulations and keep account holders’ information private and secure from would-be fraudsters and identity thieves.
How can FIs bring banking to the next generation? From Daitan:
“One of the challenges the financial services industry has in order to leverage digital channels even more is privacy. Because the banking industry is very regulated, new communication channels need clear policies and robust tools in order to avoid data leakage or fraud. Banks need tools to support them in this sense. Right now, many banks are still largely siloed when it comes to data and this prevents the quick response the market demands. So, there are a lot of opportunities, especially in the customer journey optimization.”
To bridge that chasm between the easy accessibility of customers’ preferred messaging apps and the FIs’ systems, the FIs turned to a leading secure communications platform. That secure communications platform then turned to Daitan. Together, they designed a solution.
To begin the project, Daitan advised the communications platform on how to open their online banking experience to new audiences. Through API integrations, Daitan enabled the secure communications platform to offer its financial institution clients the opportunity to adopt open banking with their customers. Those customers could then use the leading messaging apps they preferred to communicate with their FIs.
Daitan’s solution made connectivity possible between the FIs’ system and popular messaging apps like WeChat and WhatsApp. By channeling the communications through Daitan’s API and the secure communications platform, FIs can adhere to their strict compliance requirements while satisfying the demands of their account holders who want to use familiar communication channels.
Imagine being able to monitor your banking activity, execute transactions, discuss options, and approve deals from anywhere in the world with just a cell phone. Now, with the API integration developed by Daitan, account holders can monitor and act on their banking activity from the boardroom, the beach, and from the boundaries of their imagination.
FIs can rely on the security standards and controls of secure communication platforms. Customers can use the communication platform of their choice and no longer have to download and log into a FI’s specialized platform. Regulators can see that the data and the conversations are safe and secure within a platform that provides secure communication that meets the industry’s strict requirements.
Additionally, by running client communications through a secure platform, FIs can now capture the words of those conversations. With that data, they can analyze the content for red-flag fraud indicators, bolstering their fraud prevention and data loss prevention efforts.
Through developing an API integration that connected the secure communications platform to leading messaging services that people actually use, Daitan brought open banking to life for many customers who won’t even notice the seamless integration.
Use Case 2: Improve Customer Engagement
Today’s digital-first customers approach financial institutions differently than they did a decade—or even a few years—ago. Financial institutions have not followed suit. That’s about to change.
Financial institutions have enormous stores of data at their disposal. They pay to generate, store and protect that data. Imagine a scenario where they can now generate a return from that data.
With natural language processing (NLP) continually improved by machine learning (ML), financial institutions can now mine their data and discover insights about their customers, what they want, and how they want it. Using assets they already hold, financial institutions can construct customer engagement strategies supported by real-world data generated by their own customers.
A new tool to optimize customer engagement and experience at financial institutions has come to market. That tool was created through a partnership between Daitan and a leading secure communications platform.
To begin the project, Daitan gathered the data generated by the customer communications sent through the secure communications platform that customers used to talk with their financial institution. The team then analyzed the usage patterns of customers within the application. In analyzing the data, Daitan:
- Identified features that encouraged higher return rates
- Determined which usage patterns were strongly correlated with high application usage
The Data and AI team then leveraged AI to create metrics that Product Managers used to understand the patterns most likely to engage customers. Through an analysis that checked for correlations in the data and expanded to causation, the team built a roadmap that better explored and prioritized these features.
With machine learning, Daitan built algorithms that grew increasingly more accurate in predicting consumer behavior. Through leveraging Impact Mapping, Daitan linked customer communication variables to business KPIs and customer personas to predict the drivers that could lead to improvement in engagement metrics.
Finally, through Explainable AI, financial institutions could trace sales back through the customer journey and understand the factors that led to them. They could grow more confident in predicting what actions would most likely lead to not just retention, but also conversions and sales downstream.
Through the initiative, Daitan and their secure communications platform client improved customer engagement at the financial institution. By using AI to investigate and determine which platform features were most likely to bring users back to the platform the following week, Daitan was able to learn which causal relationships (not just correlating actions) existed between feature usage and user engagement.
In analyzing project results, Daitan found that <Feature A> directly increased user activity an average of +0.4 days per week during the week following that feature. For some users, that activity even increased by multiples of that average, up to three times more, for increased user activity of +1.2 days per week.
Daitan could also stack-rank the respective impacts of the features. Daitan’s analysis showed that <Feature A> was three times more impactful than the next two features. Those two features also produced significant engagement returns, around +0.5 days per week, combined.
The secure communications platform can now use these results in a number of ways. The platform plans to begin by bolstering the use of Feature A. To do this, the feature will be incentivized, advertised, and made more accessible to customers.