It’s hard to understate the transformation impact that innovation has had on the financial industry. The rise of challenger banks – also known as neobanks – ushered in a new wave of customer-focused banking services that disrupted the status quo of legacy financial institutions, forcing a new wave of competitive modernizations. In the long term, this disruption saw the industry gain a leading edge in realizing useful, usable digital tools and services.
Now, the frontiers of fintech are set to jump forward once again thanks to the evolution of Artificial Intelligence (AI).
AI is already being widely leveraged within specific solutions and across the industry, helping to analyze banking activities across disparate apps or automate incremental savings among many other use cases.
However, 2024 marks a major milestone in the overall evolution of AI and its capabilities. The latest advances will not only be able to make powerful refinements to existing use cases, but is also poised to usher in entirely new, previously unattainable business models. Furthermore, its impact on QA automation, platform refactoring, business process automation and more will also be felt.
Fintech already boasts a mature tech infrastructure and a solid foundation, meaning that new AI innovations can be developed and delivered at a very agile pace. This potential for disruption is underscored in the figures, with the global AI in fintech market anticipated to surge to $61.30 billion by 2031.
Entrepreneurs play a crucial role in this story, creating the next generation of fintech solutions and aiding the trajectory of market growth. Here are three areas where AI innovations can deliver the most impact.
Fighting fraud with automatization and a new arsenal
Unfortunately, cybercrime and fraud are an ever-present threat across the finance industry. Although solutions such as personal banking apps with facial ID recognition and two-factor authentications have improved security in some areas, the tactics employed by cybercriminals are also evolving in line with advances in technology.
Reports of personal finance fraud in the US are close to record highs thanks to sophisticated phishing scams and replica interfaces that are increasingly difficult for human users to detect.
Meanwhile, the industry has seen a worrying rise in deepfake scams. To illustrate, in February a Hong Kong banking executive transferred $25.6 million to cybercriminals. The elaborate scam saw the employee attend a video call with several senior staff members who transpired to be deepfake recreations.
AI can help the finance industry gain an upper hand and tackle the latest tactics being employed by cybercriminals, in particular by leveraging automation. By analyzing transaction patterns from transactions and unstructured sources, automation can identify highly nuanced anomalies that indicate fraudulent activities in real time.
In theory, this will enable banks to automate protection controls that are much more accurate, pinpointing fraud without impeding service delivery or frustrating customers.
Business process automation can be furthered by AI in a very real way here. Additionally, as fraudulent apps and deepfake communications are now almost indistinguishable from authentic materials, AI screening tools can be deployed to act as a first line of defense.
In this continuous game of cat and mouse, the financial industry must employ AI innovations that are equally as sophisticated as their criminal counterparts to avoid reputational damage, limit financial losses and protect customers.
Predictive analytics and Quality Assurance (QA)
AI’s role in predictive analytics has dramatically impacted the industry’s ability to analyze historical data, and combined this with current market trends to automate processes and guide decision-making. As a result, the global market for predictive analytics in the fintech sector will hit $23.9 billion by 2025.
However, AI models of the past have come under fire for being too rigid at times. To give an example, automated credit scoring algorithms may reject customers for certain products or services due to the parameters of the AI model. New generative AI models have the ability to
analyze a much broader range of data sources such as education and employment history to provide a more holistic view of an individual’s credit risk.
Aside from just approving a credit application, these models can also be used to personalize loans automatically. AI can look at huge amounts of borrower data and credit risk indicators to evaluate loan applications, figure out the best loan terms, and make instant lending choices.
AI will also play an important role in QA testing, in particular in verifying the usefulness of training data. By leveraging generative AI, financial institutions can get access to better data, improve credit access, and make business services more efficient and agile.
Asset allocation improvements, while leaning on platform refactoring
Finally, AI is set to help banks and financial institutions reshape their internal processes and generate more income from investment portfolios.
AI provides traders with sophisticated tools to analyze market trends, predict price movements, and execute trades with precision. These algorithms can analyze vast amounts of financial data in real-time, identifying patterns that individuals may overlook.
The same is true for portfolio management. AI can optimize asset allocation and minimize risk by analyzing market data, economic indicators, and investor sentiment. For multinational financial institutions that manage a vast and complex network of global holdings, AI can generate optimal investment strategies tailored to personal goals. This will lead to better diversification, higher returns, and improved risk-adjusted performance in their portfolios.
For firms that already have long-invested solutions for this area, platform refactoring – making changes to the existing codebase of an application while preserving the application’s functionality – can play a key role here.
Further, by helping investors avoid high-risk deals, AI is poised to bring about a more stable financial landscape and champion ethical trading.
The next frontiers of fintech
AI is going to define the future of fintech in 2024 and beyond. From defending against fraud to helping financial institutions better implement business process automation, AI’s integral role in the industry is clear.
The technology will drive advances that not only improve operational efficiency and security, but also redefine platform refactoring and QA automation, showcasing the technology’s essential contribution to the industry.
Imran Aftab is the CEO of 10Pearls
This article includes a client of an Espacio portfolio company
