The Rise of Fintech Personalization: How AI is Fueling Competitive Advantage


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Leading Fintech companies utilize AI-enabled technology to provide experiences that delight customers and create long-lasting relationships.

Fintech companies have long had access to a wealth of data, but the ability to process it quickly and organize it in useful ways has unlocked great potential. Data that is structured, tagged, and enhanced has changed the game and elevated personalization and engagement in product development and marketing.

The ability to use and apply machine learning and AI logic on top of transactional data, along with other experiences or knowledge about a customer, has completely changed how businesses can relate to individual customers. The capacity to use this data more effectively and target customers using that data is growing every day. Due to their past success in generating new client sign-ups, banks continue to use cookie sessions, email, and banner campaigns. The same product marketing campaigns continue to be seen by both current customers and potential leads, wasting resources and possibly offending customers tired of being pressured to buy things they already own or don’t need. But rather than displacing these tactics from the forefront, new technology enhances them with data intelligence, making them much more focused, unique, and efficient. Data processing technology and the capacity to analyze it in greater depth and detail than ever enable businesses to analyze consumer behavior patterns.

With AI, companies can compare consumers across segments, and identify opportunities, increasing campaign success rates. Fintech companies use personalization-at-scale as a key strategy to meet customer demands for hyper-relevant products and services.

Bringing genuine individualization to personal finance

Traditionally, a customer would call their financial institution and go through a short script with a call center agent to raise their credit limit. The representative would validate the amount of money the customer requested, then run an approval request to see if the requested increase could be granted. Fintechs can now understand the customer’s wider life experience and find alternative opportunities for achieving the same result by using deep learning recommendation systems. Instead of raising the customer’s credit limit, they might decide they would be better off getting a personal loan or moving their money to an account with a better interest rate. The same rules apply to businesses looking for tools for their financial services and those that benefit their clients.

The advantages of generative AI and deep learning

Fintechs can use generative AI to improve various communication channels once they better understand the customer and where they are in their financial journey. They could, for instance, improve the call center scripts, write subject email lines that are more intriguing, produce newsletters with more pertinent articles, and choose more appropriate images for their marketing campaigns. They can quickly determine what will resonate most effectively with potential or existing customers by combining deep learning with generative AI, improving conversion rates, and increasing customer satisfaction and loyalty. Data ingestion and storage are the first steps in gaining that competitive advantage. Developers can create applications for real-time personalized recommendations using the same technology using a managed ML service.

There is an increasing demand for personalized experiences in the financial services sector. With this personalization, FinTech companies can surface relevant products for their customers.

Of course, delivery time is also important. Fintechs and FIs need to reduce the time spent training models to launch products more quickly, and they also need to retrain those models on new data and inputs to keep them accurate and up-to-date. Choosing the right AI/ML and accelerated computing partner is essential to avoid slowing down the customer experience and guarantee every customer’s successful, individualized interaction.

There is such a large investment in developing AI capabilities from the big banks to the FinTech because they know it is a point of competitive differentiation. Businesses investing in deep learning capabilities for recommendation systems will likely outperform their rivals in attracting clients, providing an excellent customer experience, and gaining market share.

Scale, AI, and machine learning

AI’s capacity to use and interpret standardized data drives the insights and information that improve experiences with self-banking products and advisor relationships. It can assist advisors in developing short- and long-term plans, visualizing scenarios to aid in making quick, informed decisions, and optimizing portfolios and strategies for their clients.

By enabling the ability to gather data from a variety of extremely disparate sources, synthesize it, and process it, generative AI will allow this scale to increase even further. However, humans will always play a key role in ensuring these tools are tuned properly, from providing objective data as clean as possible to fine-tuning algorithms and catching inevitable AI model drift as an algorithm continues to run.

Data scientists must ensure it is targeted on the appropriate scenarios for FinTech and tuned to the proper types of experiences clients or companies want to drive. The vast amounts of transactional and non-transactional customer data that banks have access to can be used to generate a reliable source of income. Banks have long targeted customers with customized offers, but thanks to AI, they now have new tools to provide personalization at scale. Data analytics insights can be turned into useful products and services that can assist customers in managing their finances, making better investments, and making more informed decisions about what to buy—offering their clients a comprehensive and meaningful banking experience.

Process optimization through intelligent automation

By digitizing the manual entry of client information from various sources, robotic process automation can assist banks in lowering the risk of manual errors. Thus, automating processes enables bank executives to concentrate more on client relationships and care. Intelligent automation can help banks manage risk and defend themselves from bad actors by optimizing back- and middle-office operations. Banks can quickly adapt to crises while operating effectively using a predictive risk management approach.

Also Read: AI in Fintech: How Advanced Generative Models are Driving Innovation

Unlocking Data and AI’s growth potential

Banks will need to use data and technology to serve their customers better to survive the pandemic’s effects. Operational intelligence can help banks drive day-to-day tasks with data, advanced analytics, and AI-powered tools. AI can support conventional banking operations in various ways, including forecasting market outcomes, risk management, and process efficiency management.

Whoever provides the most timely, relevant, and individualized experiences will have the most influence over the future of banks. Banks still have the advantage of being the subject matter experts even though new-age FinTech and challenger banks have done that to some extent and gained customers. Adding artificial intelligence to this expertise gives banks an unfair advantage in providing meaningful customer experiences essential for developing in a cutthroat market.

Swapnil Mishra
Swapnil Mishra
Swapnil Mishra is a global news correspondent at TalkCMO, with over six years of experience in the field. Specializing in marketing technologies, Swapnil has established herself as a trusted voice in the industry. Having collaborated with various media outlets, she has honed her skills in content strategy, executive leadership, business strategy, industry insights, best practices, and thought leadership. As a journalism graduate, Swapnil possesses a keen eye for editorial detail and a mastery of language, enabling her to deliver compelling and informative news stories. She has a keen eye for detail and a knack for breaking down complex technical concepts into easy-to-understand language.


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