Financial technology, known as FinTech, is the technology used by consumers to manage their finances. Examples include cash transferring app Venmo, embedded finance solutions such as Shopify Balance, and cryptocurrency wallet services like Abra. A recent article posted by FinTech provider Plaid notes, “The percentage of US consumers using technology to manage their finances jumped from 58% in 2020 to 80% in 2022—meaning more people now use FinTech products than social media.”
Benefits of FinTech include granting users more opportunities to manage, share, save, and invest their money, including those users historically underserved by traditional financial institutions. The following video by the Boston Consulting Group points at use cases of generative AI in finance. As you’ll see, benefits are concisely explained and gains are stressed.
Like creators of other digital advancements, FinTech developers must determine ways to make their products as useful and harmless to consumers as possible. Doing so includes addressing important elements like security, data accuracy, compliance, asset management, and neural network structures. In the following sections, we explore how FinTech companies are using AI to address these critical priorities. But first we explore what generative AI is and why it plays such an important role in the emerging FinTech field.
#1 Amplifying Security with Risk and Fraud Detection
In our recent white paper 8 FinTech Trends: From Web3 to Open Banking, we discuss a few use cases for generative AI agents in the role of securing data and finance pipelines. Justice Erolin, BairesDev’s CTO, suggests that generative AI agents could help solve security issues in large financial systems built on legacy technology. As financial and banking institutions go through digital transformation in the era of AI, emerging solutions could help accelerate the safe adoption of new technologies.
Generative AI helps with fraud detection by analyzing large volumes of financial data and identifying normal patterns and anomalies that indicate deceptive activities. Fraud detection models can become more effective over time as they learn from historical data, this is something BairesDev’s client, Abra, knows well.
Wille Wang, VP of Engineering at Abra, stated in our FinTech white paper that “AI will have a role in the gathering and assessing of information and risk for particular protocols. It could take the shape of an alerting system. If unusual movements are happening on a particular protocol, we can be the first to know and prevent losing money.”
Within the FinTech industry, fraud detection takes many forms, including cybersecurity, blockchain security, DDoS protection, DNS security, user authentication, and optimizing communication interfaces. Using generative AI-based tools, companies can boost their cyber defenses, ensuring the safety of sensitive financial data.
#2 Enhancing Data Accuracy and Quality Assurance
FinTech providers must be ready to address the issue of AI inaccuracy, given how important accurate data is to critical decision-making on the part of FinTech users. A Forbes Technology Council post states, “Since generative AI occasionally produces errors, or ‘hallucinations,’ quality control is central to avoiding inaccurate outputs.” A focus on data accuracy is critical for FinTech providers because “[a]chieving accuracy is vital…for establishing the trust necessary for long-term success between financial institutions and their clientele.”
Related to data accuracy is data quality. To perform most effectively, generative AI requires massive amounts of high-quality data. To ensure high quality, FinTech providers must monitor data when it is extracted from a source, transformed into a usable resource, and loaded into a data warehouse. This data processing method is known as ETL. Generative AI can contribute to it by filling in missing data points and identifying outliers in financial data sets.
Generative AI models can perform these tasks in a way that protects sensitive personal information. They do so by generating synthetic data that shares the statistical properties of the original data, which can stand in for the original data during analysis operations. This action is critical for maintaining data privacy, which is paramount for financial transactions.
#3 Unlocking Compliance and Regulatory Insights
Generative AI can help companies maintain compliance with pertinent regulations by analyzing requirements and identifying areas where companies may be in violation of them. This process is critical for companies that want to avoid fines or penalties and maintain trust with regulatory authorities and the public.
One area of compliance that has received a good deal of attention recently is the notion of “glass box transparency.” In Europe, the Artificial Intelligence Act provides a set of rules for transparent AI. The Act requires “makers of A.I. systems like the ChatGPT chatbot to disclose more about the data used to create their programs,” according to the New York Times, and requires disclosures about whether content was generated by AI.
Such transparency is important, according to the Forbes Technology Council post, because it “enable[s] users to understand and explain how decisions are made and ease[s] regulators’ concerns about the development and deployment of AI technology.”
If you want to explore generative AI solutions for your FinTech projects, learn more about our artificial intelligence development services.
#4 Redefining Market Analysis and Asset Management
Generative AI can be trained on historical financial data including stock prices, market trends, and customer preferences, and used to replicate market behavior, identify patterns, analyze trends, and predict what might happen next. Generative AI can be deployed in the following ways:
- Market data analysis: Generative AI models can analyze historical market data and predict future asset prices.
- Portfolio optimization: Generative AI can simulate various economic scenarios and provide sample data to assess the risk associated with various portfolio allocations and diversification strategies.
- Alternative data analysis. Generative AI models can develop insights using alternative data sources, including social media sentiment, news articles, and satellite imagery.
- Natural language processing: Natural language processing (NLP) uses financial news, reports, and earnings call transcripts to identify relevant data points that can support the process of making asset management decisions.
Algorithmic trading: The algorithmic trading process involves generative AI analyzing market data, identifying patterns, and making trading decisions based on the resulting information.
Source: MarketResearch
#5 Leveraging Neural Networks
Neural networks are a superset of AI algorithms, consisting of collections of interconnected processors, known as neurons, that perform input, analysis, and output of information. Prediction accuracy is improved with higher-quality training information. Neural networks can be used for predictive purposes, such as to project stock market performance, issue bond ratings, and assess debt risk.
One of the most useful ways neural networks can be used within FinTech is for risk management, which is analyzing the risk associated with specific financial transactions. For example, companies that offer loans want to ensure that the individuals they loan to are likely to pay back the entirety of the amount borrowed on time because those are the loans that are most profitable for them.
Risk assessment algorithms take into consideration various factors to help make loan determinations. The factors include credit scoring, rent payment history, and debt load to determine whether an individual is a good loan risk.
Neural networks are also used to perform some of the other functions described throughout this post. They include fraud detection, algorithmic trading, customer service, portfolio management, market sentiment analysis, predictive analysis, cybersecurity, and personalized financial recommendations.
The Growing Need for Generative AI in FinTech
FinTech is a quickly growing market that entails a wide range of complex considerations, from consumer privacy and security to consumer protection regulations and responsibility to the public at large. Given all these factors, technologies that can help drive company success are highly useful. In addition to the transformative uses of generative AI mentioned above, it can help in the following ways.
Chatbots for Financial Institutions
Chatbots are AI-based programs that can communicate with FinTech users and help them perform tasks. Chatbots have evolved over the years to do more than just respond to simple questions with straightforward answers. They can now generate context-driven content for actions like helping users choose investments or make other financial decisions.
Artificial Intelligence & Personalized Financial Advice
Generative AI can analyze user histories, stated financial goals, and risk tolerance to offer personalized financial advice, such as relevant product and service suggestions. Such tools can give consumers the information they need to make smart financial choices about things like savings, investments, and financial planning.
Financial Sector Loan Processing
In addition to assessing creditworthiness for loans, as discussed above, generative AI can help to process loans after an applicant has been approved. Companies can use it to automate tasks such as verifying documents and creating loan agreements. This process helps to streamline the process, improving the customer experience and saving the company money.
Customer Service
Chatbots and virtual assistants, both based on generative AI, can provide automated customer service. Using NLP, these tools can be deployed to provide personalized responses and assistance to customers for basic requests. They can also refer customers to the right human representative to help them in the case of more complex issues.
Enhanced Data Augmentation
Generative AI can help augment databases with data needed for training and validation. This process enables companies to shore up datasets that might be limited in their capabilities based on scarce or imbalanced data. It improves the performance of datasets, which makes them more relevant for real-world scenarios and more accurate in their predictions.
Regulatory Compliance
Financial companies must comply with regulations that are meant to protect consumers. Generative AI can help by validating data, monitoring transactions, onboarding customers, sharing data between authorities, reporting transactions, analyzing audit trails, and monitoring compliance requirements.
Acknowledging the Risks of Generative AI
Generative AI is important in FinTech because of its capabilities in automating complex tasks, streamlining processes, personalizing services, improving customer care, mitigating risks, and improving decision making. These functions are useful, but FinTech companies must also be aware of the risks of generative AI, as follows:
- Inaccuracy: Generative AI models can deliver inaccurate results, sometimes known as hallucinations. They understand the way language flows but not the way the world works. One example of an inaccurate generative AI result that is widely known is that of legal professionals using a generative AI platform to develop legal briefs, which turned out to be factually wrong.
- Lack of transparency: Generative AI models are complex, so explaining outcomes such as loan decisions to customers and other stakeholders can be difficult. This phenomenon is known as explainability, and a lack of it can cause legal or regulatory problems for FinTech providers, as such decisions must be transparent so their fairness can be evaluated.
- Privacy: One property of generative AI is its ability to memorize. Unfortunately, that means it sometimes retains information users would prefer it didn’t. If a generative AI model receives specific data, it might recall and later output certain elements of that data that are personal or private to individuals or companies.
- Copyright challenges: Another challenge that emerges when generative AI models use information found on the internet to create responses to user queries is that much of that content is copyrighted. Yet, generative AI models don’t follow the rules for use of such content, such as proper attribution.
All these risks make the elements described in the following sections highly important to consider.
The Human Element
As we have seen in the sections above, generative AI in FinTech can provide a wide variety of benefits for those who provide and use FinTech products. But, to ensure the technology behaves in ways that are productive, humans must closely supervise its use and the outcomes that result.
Broader ethical considerations, especially when handling customer data, are another area in which human oversight is pivotal. It’s imperative to recognize and rectify potential biases, like the gender bias discussed above. If unchecked, these biases can manifest into larger systemic issues that not only exacerbate existing inequalities and hinder social mobility but also erode trust in specific institutions or the financial system as a whole, especially in the realm of financial technologies.
Generative AI can help the financial services industry to create opportunities for many consumers, including those who may not have had access to useful financial tools in the past. However, to provide the greatest value and avoid challenges with things like bias and ethical issues, FinTech providers must keep a careful watch on the generative AI solutions they use.
