AI In FinTech: Impact And Use Cases

The FinTech industry is experiencing a seismic shift with the integration of cutting-edge technologies. Artificial Intelligence (AI) and Machine Learning (ML) provide ample opportunities for the industry to streamline its operations, work more efficiently and deliver cost-effective and optimized solutions. Experts believe integrating AI and ML technologies will make Fintech more accessible, transparent, and secure.

Let’s trace the growth of AI and ML in the financial domain and explore how these technologies empower innovation and customer experience improvements.

Well before ML and AI could be implemented in the financial industry, accurate and structured data was the most important aspect underlying their adoption.

Once data storage and maintenance capabilities have grown, together with advancements in computing power, this set the stage for large-scale data processing and analysis, making it feasible to leverage AI and ML techniques. The availability of vast amounts of structured and unstructured data in the finance industry has also fueled training and improving AI and ML algorithms.

The increasing demand for more personalized and efficient financial services and the need to mitigate risks and detect fraud have also incentivized the adoption of AI and ML technologies in the financial sector. Then, regulatory frameworks and industry standards have evolved to accommodate the use of AI and ML, further facilitating their integration into financial institutions.

Briefly, the timeline for the adoption of AI and ML in the FinTech sector looks like this:

• In the late 2000s and early 2010s, AI entered the financial industry through chatbots and models used for fraudulence detection.
• Around the mid-2010s, more sophisticated AI and ML models started being applied in areas such as algorithmic trading, credit scoring, and customer service.
• In recent years, AI and ML have become integral to various financial applications, including chatbots, robo-advisors, and smart risk assessment systems.

Based on recent reports about the use of AI and ML in the financial sector, 62% of financial services providers state that the ability to use data insights to understand better their customers makes a key factor for implementing these technologies.

Here are some customer-focused uses of AI and ML in the financial sector.

Credit Scoring

In the past, credit reports were the primary basis for credit quality analysis. Now, additional sources of unstructured and semi-structured data provide a more nuanced understanding of someone’s creditworthiness. By applying ML algorithms to new datasets, qualitative factors like willingness to pay and consumption behavior can be assessed, enhancing the accuracy of credit evaluations.

The integration of ML algorithms in credit scoring has also expanded access to credit. Traditional credit scoring models used to require sufficient historical credit information for someone to be considered scorable. This posed a challenge for borrowers without an established credit history, leaving them unable to obtain credit.

ML algorithms overcome this limitation by leveraging alternative data sources and evaluating creditworthiness based on a broader set of factors. These include utility and rent payments, mobile phone usage, spending habits, social network behaviors, online transaction data, and cash flow patterns. As a result, more individuals, including those previously excluded, have opportunities to access credit and build a credit history.

Insurance Pricing

The use of ML in the insurance industry has proven to be highly valuable in analyzing complex data, leading to cost reductions and improved profitability.

By leveraging AI and ML, insurance companies can streamline the underwriting process. This enables agents to efficiently analyze large datasets collected by insurance firms. Through this analysis, ML algorithms can identify and highlight cases with higher risks, enabling the company to take preventive measures.

Additionally, by incorporating real-time and highly granular data, such as online shopping behavior and telematics, insurance companies can gain valuable insights and price insurance products more accurately. Integrating ML in these areas improves overall pricing accuracy and enhances claims processing.

Chatbots

Chatbots employ neuro-linguistic programming (NLP) to engage with users through text or voice interactions. In FinTech, chatbots are mainly used within mobile applications and social media channels. They provide balance updates, issue alerts, and address basic customer inquiries. By leveraging chatbots, FinTech firms benefit from cost savings and enhanced communication with customers, ultimately contributing to increased profitability.
Financial Education
AI-powered financial platforms can provide personalized financial education and guidance to users of all backgrounds. These tools can help individuals build their financial literacy and make informed financial decisions, contributing to their creditworthiness in the long run.

Personalization

With AI-driven analytics, FinTech businesses can analyze vast amounts of customer data to offer tailored services based on customer preferences and habits. At the very least, chatbots are available 24/7 to respond to customer service issues and provide financial advice.

AI can also personalize the investment experience and create customized user portfolios through machine learning models trained on customers’ spending behavior and investment knowledge. These AI-driven predictive models can evaluate customer financial goals, risk tolerance, and investment horizons to tailor investment plans that meet their specific needs.

With an estimated 3,150% growth rate in terms of successful chatbot interactions between 2019 and 2023 and an estimated 862 million hours saved for businesses in the future, AI-driven analytics boost customer satisfaction and contribute to building stronger, long-lasting customer relationships.

Many fintech companies fall victim to the fraud game, but the good news is that anti-fraud AI implementation is on the rise. According to NVIDIA, 10% of financial service providers used AI-based anti-fraud technology in 2021, with 31% doing so in 2022. Here are some of the AI solutions currently used for preventing fraud:

• Data collection & processing. ML models and AI systems gather and process large volumes of data related to financial transactions, customer behavior, market trends, and external factors. When a transaction mismatches the pattern, the technology spots it before completion.
• Real-time monitoring & push notifications. By analyzing and processing data as it flows, ML models and AI systems can quickly identify suspicious activities and generate alerts for further investigation. When someone tries to buy something with your debit card number, either online or not, anti-fraud AI can detect and block the transaction. Then, it can notify you and ascertain if that’s you. These notifications can be emails or text messages, but due to the rise of voice AI, they can also be calls.
• Predictive & behavioral analytics. AI can analyze user behavior and transaction patterns and create individual customer profiles. Whenever an unusual activity deviates from these profiles, AI can trigger alerts and prompt further investigation. The same goes for predictive analytics – AI can detect potential risks and fraud trends based on the gathered data, enabling prevention measures.

AI in FinTech has revolutionary potential, but not without digital marketing agencies to help implement these innovations.

Using AI-powered chatbots and virtual assistants can transform customer targeting and engagement. Whether it’s behavior & financial goals analysis or personalized recommendations and investment advice, these services improve customer insights for better decision-making and boost loyalty.

Besides, AI unlocks the opportunity for powerful omnichannel marketing. With a large count of digital touchpoints, marketing agencies can keep their messaging and branding consistent with the help of AI. AI-powered tools can make data-driven decisions about target audiences, content, and budgeting.

As a result, marketing efforts get seamlessly integrated across websites, mobile apps, social media, and emerging channels. Add AI-driven content creation, and there’s a steady flow of content that drives leads and saves resources.

Regarding security, AI technology can analyze vast amounts of data to identify patterns, detect anomalies, and generate meaningful insights that can help FinTech companies make more informed decisions. Here, digital marketing agencies can step in to help establish transparent data practices and communicate the commitment to data protection effectively. Trustworthiness in data handling makes a competitive advantage for FinTech companies seeking to attract new customers in an era of growing data privacy concerns.

Leveraging AI empowers FinTech companies to provide seamless services to their customers while reducing fraud and cyber attacks. With the amount of data increasing exponentially, the future of FinTech will likely be more heavily reliant on AI-powered solutions, which requires a holistic approach that involves collaboration between digital marketing agencies and fintech companies. This collaboration ensures a deep understanding of business objectives, target audiences, and available data. As a result, effective implementation and utilization of AI and ML technologies in marketing strategies can reshape the future of the financial industry.