To succeed in our digitally first society, today’s FinTechs and financial institutions (FIs) require state-of-the-art technologies. For businesses in the financial sector willing to adopt the newest machine learning and natural language processing technology, AI-enhanced breakthroughs hold the promise of revolutionary change. However, FIs must rethink the role of data in their operations and strategies in order to embrace this brave new world.
Fintechs have already started to erode the glacial shift in how financial institutions communicate with their clients. Emerging fintechs have taken notice of opening banking, which was first introduced by third-party personal financial management software. Banking clients can now use the platforms of their choice to communicate with their financial institutions thanks to application programming interfaces (APIs).
FIs may now examine client conversations and gain insights about how to best increase engagement thanks to the development of new natural language processing (NLP) technologies. Financial institutions may now decide how to effectively reach their consumers, when to do so, and how to organize and craft their communications with them thanks to machine learning-informed algorithms.
In order to develop comprehensive, data-driven strategies that promote demand development and improved consumer experiences, fintechs and financial institutions today require data.
Those who successfully integrate AI into their customer experience and engagement plans aim to capitalize on the following main advantages of their digital-first strategy:
New consumer markets that prioritize digital.
Reduced expenses for acquiring new clients.
Increased adherence to and usage of purchased goods.
More chances for cross-selling.
AI facilitates data navigation and use for FIs and fintechs, but what about compliance? Data privacy and loss prevention are just as important as any other strategic endeavor in a highly regulated sector like financial services. However, this does not exclude FIs from using data to inform their choices and tactics. FIs can also develop data-driven strategies and make data-driven decisions if they have the appropriate tools. If they build the appropriate relationships and create the right products and tactics, they can reach their customers when, when, and how they want.
Customers return with their business when you meet their needs. According to a 2020 McKinsey Report, banks with the greatest customer satisfaction ratings saw an 84% faster growth in deposits. Financial firms that properly handle the emergence of artificial intelligence and its advancements stand to gain greatly.
First Use Case: Open Banking
Challenge
According to a 2017 OpenMarket study, over half (53%) of today’s millennials would rather text than make voice calls. Furthermore, 59% of respondents claim to receive text notifications with account activity, payment reminders, and fraud alerts.
The financial sector is trying to catch up. FIs seek to shed their stuffy, conventional image and embrace open banking practices for today’s clients in order to gain the business of today’s younger generations, who are becoming more and more prominent.
Open banking technologies offer to extend communication channels beyond phone calls and emails in order to provide its account holders with increased financial and communication transparency. FIs can find new methods to connect their own apps and services with today’s communication platforms by using open APIs.
The difficulty with open access, however, is in FIs’ ability to adhere to current laws and protect account holders’ data from identity thieves and potential fraudsters.
How can FIs help the next generation of bankers? From Daitan:
“Privacy is one of the obstacles the financial services sector has in its efforts to further utilize digital channels. The banking sector is highly regulated, therefore new channels of communication require strong tools and transparent standards to prevent fraud or data leaks. For banks to be supported in this way, they require instruments. Many banks are still mostly data-segregated at the moment, which hinders their ability to react quickly to market demands. Thus, there are numerous opportunities, particularly in the optimization of the client journey.
Steps Done
The FIs resorted to a top secure communications platform in order to close the gap between their systems and the convenient accessibility of their clients’ favorite messaging apps. The platform for encrypted communications then looked to Daitan. They came up with a solution together.
Daitan started the initiative by giving the communications platform advice on how to introduce new audiences to their online banking experience. Daitan made it possible for its financial institution clients to embrace open banking with their clients by integrating APIs into the secure communications platform. After that, those clients might contact with their FIs using the popular messaging apps of their choice.
Connectivity between the FIs’ system and well-known messaging apps like WeChat and WhatsApp was made possible by Daitan’s solution. FIs may serve the needs of their account holders who prefer to use recognizable communication channels while yet adhering to their stringent compliance requirements by routing the communications through Daitan’s API and secure communications platform.
Findings
Imagine using a cell phone to monitor your banking activities, complete transactions, talk about choices, and approve deals from any location in the world. Account holders may now keep an eye on and take action on their banking activities from the boardroom, the beach, and beyond their wildest dreams thanks to the API integration created by Daitan.
FIs can depend on secure communication platforms’ security measures and standards. Customers are no longer required to download and log into a FI’s specialized platform; instead, they can utilize the communication platform of their choosing. Within a platform that offers secure communication that satisfies the stringent criteria of the sector, regulators may observe that the data and conversations are safe and secure.
Furthermore, FIs can now record customer communications by using a secure platform to run those chats. They may strengthen their efforts to avoid fraud and data loss by using that data to examine the content for red flag fraud signs.
Daitan made open banking a reality for many clients by creating an API integration that linked the secure communications platform to popular messaging services that people genuinely use. Many customers won’t even be aware of the smooth integration.
Use Case 2: Boost Interaction With Customers
Challenge
Financial institutions are approached differently by today’s digital-first consumers than they were ten or even a few years ago. Financial organizations haven’t done the same. That will soon change.
Financial firms have access to vast data repositories. To create, keep, and safeguard such data, they pay. Suppose that they are now able to use the data to provide a return.
Due to the ongoing advancements in machine learning (ML) and natural language processing (NLP), financial institutions are now able to mine their data and learn more about their clients’ preferences. Financial organizations can use their existing assets to build customer engagement plans that are backed by actual data produced by their own clients.
A brand-new product has hit the market to help financial institutions improve client experience and engagement. An industry-leading secure communications platform partnered with Daitan to develop that product.
Steps Done
In order to start the project, Daitan collected the data produced by the communications that clients transmitted to their financial institution via the secure communications platform. After that, the group examined how users interacted with the application. When examining the information, Daitan:
Features that promoted higher return rates were identified.
Identified the usage patterns that were most closely associated with heavy application use.
Then, using AI, the Data and AI team developed metrics that Product Managers could use to identify trends that would most likely interest customers. The team created a roadmap that more thoroughly examined and ranked these attributes after doing an analysis that looked for correlations in the data and went on to examine causation.
Daitan developed algorithms that used machine learning to predict customer behavior with ever-increasing accuracy. In order to forecast the factors that can result in an improvement in engagement metrics, Daitan used Impact Mapping to connect customer communication variables to business KPIs and customer profiles.
Lastly, financial organizations might track sales back through the customer journey and determine the contributing elements with Explainable AI. They might become more adept at anticipating which behaviors would most likely result in downstream conversions and sales in addition to retention.
Findings
Through the campaign, the financial institution’s customer engagement was enhanced via Daitan and their secure communications platform client. Daitan was able to discover which causal correlations (rather than merely connecting actions) existed between feature usage and user engagement by employing AI to research and identify which platform features were more likely to entice users to return the following week.
According to Daitan’s analysis of the project’s outcomes, directly raised user engagement by an average of +0.4 days per week in the week that followed. When user activity increased by +1.2 days per week, certain users’ activity even increased by multiples of that average, up to three times more.
Additionally, Daitan might stack-rank each feature’s impact. has three times the impact of the next two features, according to Daitan’s analysis. Together, those two additions generated notable engagement returns of about +0.5 days per week.
These findings can now be applied in various ways to the secure communications platform. The platform intends to start by encouraging more people to use Feature A. The functionality will be promoted, rewarded, and made more user-friendly in order to achieve this.