FinTech service is a highly regulated field. How generative AI will affect it is a point of argument. If we divide based on short and long periods, in the short term Gen AI will focus on providing a seamless customer experience and better onboarding process. In the latter, the impact could be seen in a much wider context, i.e. lower transaction friction.
From 2023 to 2032, the market is expected to increase at a staggering compound annual growth rate (CAGR) of 22.5%, continuing its explosive ascent. Following this exciting voyage, the market is projected to grow to an enormous $6.256 billion by 2032. What is the cause of this amazing growth?
FinTech companies are embracing the concept of generative AI. They are using it to increase their operating engines, reduce costs, and develop novel new products and services.
Digital banking and mobile payments are revolutions, not just trends! This modification recognizes the need for financial services that are both intelligent and personal.
Friction-ridden economies are typically less innovative, based on informal trust mechanisms such as family, and concentrated around huge, vertically structured enterprises.
Some Trends Happening in the FinTech Industry
In the FinTech software development industry, the term “transaction friction” is reduced primarily due to three main trends, i.e. advancement of open banking, digital financial products, and real-time payment. Combining those developments could be the key to releasing generative AI’s transformational potential for the financial services industry.
1. Digital financial products
The global pandemic significantly accelerated the digitalization of financial services as more businesses moved online. Using digital tools, including online notarization with DocuSign or meetings using Zoom, removed the need for physical presence when processing legal paperwork, opening bank accounts, or purchasing homes.
A new generation of tools to further automate financial life processes has been made possible by the increased acceptability of online products. Changing money across accounts, for instance, might optimize investment accounts for taxation, increase interest payments, and identify financial crime.
Furthermore, people who currently lack access to financial advice will benefit greatly from removing the barriers to getting it, notably the elderly and people living in rural areas who often have unique financial needs.
2. Open Banking
By making financial data more widely available, Open Banking enhances the personalization of financial products. With the global adoption of Europe’s PSD2 open banking efforts, which gave individuals greater control over their financial data and pushed financial institutions to innovate, open banking projects have gained momentum.
Financial institutions are now compelled to provide APIs to allow FinTech start-ups to expand on their data. The FinTech ecosystem’s foundational infrastructure has seen significant investment resulting in the spread of open-banking regulations. This is essential for use cases involving Generative AI, particularly in consumer applications, as it makes sharing data easier that may be utilized to create customized products.
3. Adoption of real-time payments
Finally, the spread of government-backed real-time payment networks is lowering the cost and speed of money transfer friction. Early adoption of emerging economies in Brazil (PIX), India (UPI), and others have spurred innovation in domestic account-to-account payments. Hence decreasing the requirement for traditional banking institutions to handle money transfers and storage.
Money will soon be able to flow anywhere at a far cheaper cost in almost real-time thanks to worldwide links between networks now growing regionally, even though the trend will take a few years to pick up steam.
Enabling AI to function at a speed and scale that is presently unthinkable requires freeing up payments and drastically cutting interchange fees. Quick and effective payment networks will be helpful to those in developing economies or with smaller savings.
Generative AI in FinTech: What are the Opportunities?
1. Opportunities in Digital financial products
According to Marqeta research, more than half of younger consumers want Generative AI to assist them in managing their finances. Generative AI holds great potential as it can comprehend consumer preferences, spending patterns, and financial objectives, enabling it to offer tailored financial solutions or suggestions to any individual.
We anticipate a rise in demand for more individualized guidance and improved customer care as wealth management products become more commonplace. Soon, financial data can be used to support corporate training, broaden financial education, and distinguish services via generative AI. Additionally, it will raise the earning potential of individual advisers and improve the productivity of current teams.
2. Opportunities in Open Banking
Using consumer data, the initial set of applications developed on open-banking rails sought to increase credit boxes and improve consumer risk underwriting. FinTechs, for instance, sought to better assess payback risk for customers with little or no credit history by using cash flow data. The consumer’s cash flow data might be examined with a thorough understanding of cash inflows and outflows historically and in real-time, compared to depending on a FICO score.
The idea makes a lot of sense in theory. Organizing, cleaning, and analyzing the underlying financial data is difficult. Many of these activities can be made more scalable by using generative AI, which can identify patterns in language and adapt them based on the inputs of its models.
3. Opportunities in Real-Time Payment
The payments industry is home to many generative AI developments that promise to drastically lower transaction friction and give consumers smooth, safe experiences. These advances in AI are well-positioned to benefit several new payment trends dependent on digital technology.
Along with other cutting-edge methods of transferring and protecting wealth, these developments include digital wallets, mobile payments, and contactless and biometric payments. They might bring in a new age of quick and easy payment options.
For instance, Visa recently unveiled RTP Prevent, which uses artificial intelligence (AI) to quickly identify possible hazards for financial institutions processing real-time payments by analyzing transaction data in real-time.
Generative AI in FinTech: What are the Risks Involved?
Financial authorities are urged to improve institutional capabilities, oversight, and monitoring of the development of generative artificial intelligence in light of the International Monetary Fund (IMF) paper on “Generative Artificial Intelligence in Finance: Risk Considerations”.
1. Robustness
Strong AI performance in the financial system is quickly emerging as a critical concern for preserving financial integrity and stability and, eventually, public confidence. Robustness addresses concerns about how accurate AI models produce results, especially in dynamic environments.
It also addresses the governance of AI system development and operation, to prevent unethical use and its harmful, biased, and exclusionary impacts.
Reducing false signals during structural shifts is a major difficulty for AI/ML algorithms because of their predictive nature. When applied to a reasonably stable data environment that generates consistent signals, AI/ML models function well, allowing the models to accommodate changing data patterns without appreciably lowering forecast accuracy.
But when previously dependable signals start to falter or behavioral correlations drastically change, making predictions less accurate, AI/ML algorithms have a harder time performing their job.
2. Use of Synthetic data
Practical business objectives and regulatory requirements are the main forces behind the growing usage of synthetic data. Because synthetic data cannot be linked to any individual or group, it is an appealing answer to data privacy concerns in the context of AI/ML training, especially in highly regulated industries like financial and health services.
Along with helping to create more resilient, understandable models that better adhere to legal standards, synthetic data also presents the chance to reduce imbalances and biases in real data.
Furthermore, synthetic data offers a financially viable training data substitute for companies without substantial access to proprietary real data, as the current data ownership landscape is heavily biased in favor of well-established technology and industry incumbents.
3. Cybersecurity
The cybersecurity landscape is faced with major new challenges from GenAI. The potential consequences of this developing technology include the creation of increasingly complex phishing emails and communications, as well as chances for criminals to pose as people or companies, which increases identity theft and fraud.
Deepfakes can cause significant harm to individuals and organizations by producing more realistic-looking films, audio, or photographs.
GenAI models may be susceptible to input attacks and data poisoning. By including unique features in the training data set, data poisoning attacks aim to affect AI models during the training phase and potentially compromise training accuracy or conceal malevolent behaviors that require unique inputs. Similar to input attacks, which aim to modify the AI models while in use.
4. Embedded Bias
GenAI models are trained on various data types, including online text, which inherently introduces biases from human experience. When it comes to AI/ML, operators work to reduce embedded bias by choosing the data that will be used to train the system carefully.
However, considering the volume and variety of the training data, this procedure might be much more difficult for GenAI. Furthermore, the method and algorithm utilized to produce the GenAI responses may be biased.
GenAI uses its training data to construct textual answers, or “new content,” depending on the accuracy likelihood of each answer, in contrast to AI/ML, which uses training data for predictions.
The issue of data bias in GenAI may make it more difficult for the financial services industry to accept and employ. Financial organizations may find that using GenAI to analyze transactions and quickly identify questionable ones is an affordable and efficient method of profiling their clientele, including for risk management.
However, if proper protections are not taken, relying too much on GenAI-generated profiles can result in unfair or erroneous client assessments. GenAI-based transaction monitoring models will require the addition of appropriate human judgment.
5. Data Privacy
Financial companies looking to integrate publicly available GenAI technology into their operations face serious privacy concerns. Through the process of automatically “opting in,” these GenAI systems continuously use user input to learn and improve their responses.
Because of this automation, there is a greater chance that private information and sensitive financial data supplied by employees of financial institutions to the GenAI will be compromised. Numerous GenAI systems frequently say that they cannot guarantee the security and privacy of the data and information that users supply.
While some privacy issues with public GenAI are anticipated to be resolved, enterprise-level GenAI systems are being created in part to address these issues. These enterprise-level GenAI systems may improve the data security of financial firms. Still, there are unresolved privacy issues. They are related to the nature of GenAI’s processing power over various data formats, including information scraping from the internet and online platforms (like social media).
These data make GenAI a valuable tool for financial institutions to use for fraud detection and credit evaluation. However, there’s a danger that by utilizing this function, unintended collection and use of personal information that would have required express authorization would occur.
Final Thoughts
Although they should be used carefully, GenAI technologies have a lot of potential for applications in the financial sector. Significant efficiency gains, enhanced customer satisfaction, and strengthened risk and compliance can be achieved using GenAI.
However, the inherent dangers in GenAI might seriously jeopardize the stability and credibility of the financial sector, which would eventually erode public confidence. Although using enterprise-level GenAI systems could help reduce some of the dangers associated with public GAIs, smaller financial institutions might need help finding this cost-effective solution.
Interim measures are required, however, the regulatory policy will change over time to help direct financial institutions’ usage of GenAI applications. To reduce potential hazards, human oversight is required when utilizing GenAI in financial institution operations. It includes distinguishing between the use of AI for analysis and recommendation versus the deployment of AI systems capable of making and carrying out decisions.
The institutional capacity of prudential oversight bodies should be reinforced, and they should step up their surveillance and monitoring of technology development, keeping a careful eye on how it is used in the financial industry. To achieve this, they should cooperate with regional and global authorities and enhance communication with stakeholders in the public and commercial sectors.
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