Top 10 Applications of Artificial Intelligence Reshaping FinTech

Artificial Intelligence (AI) and its tangible applications are no longer confined to the realm of distant visions depicted in science fiction. Instead, the widespread availability of data, the affordability of computing capabilities, and the accessibility of enhanced networking bandwidth have positioned AI as a central force in today’s digital economy. This technology is permeating nearly every sector, each embracing it to varying degrees of maturity.

In this landscape, certain sectors stand out as ideal environments for the almost instantaneous adoption of AI, and Financial Services is at the forefront. Traditionally burdened by high workloads, stringent compliance requirements, and an ever-growing demand from customers for enhanced convenience, the industry has discovered a formidable ally in AI-driven FinTech.

Uses Cases of Artificial Intelligence in FinTech

1. Enhanced Fraud Detection:

The financial services sector stands as a prime target for fraudulent activities and cyber intrusions on a global scale. The increasing digitalization of the value chain has expanded the attack surface, leading to a surge in fraud attempts against financial services companies. In 2021, TransUnion reported a staggering 150% increase in such fraudulent activities, a trend that shows no signs of abating. As malicious actors outpace manual responses from financial services, intelligent FinTech emerges as a formidable ally in the fight against scammers.

Artificial Intelligence (AI) empowers FinTech to rapidly correlate, analyze, and interpret millions of data points, exposing anomalies and suspicious behavior within transactions at an unprecedented scale. With AI, it becomes possible to identify specific triggers and patterns, preempting fraud attempts automatically. Take, for instance, the significant threat of identity theft, causing substantial losses for the financial services industry. ZOOPSTACK, a FinTech leader, has pioneered an AI-based solution that enhances face detection, matching, and passive liveness checks during video Know Your Customer (KYC) processes. This ensures that financial services businesses engage with actual individuals through live sessions, thwarting attempts with pre-recorded videos or photographs. ZOOPSTACK’s solution, incorporating advanced augmentation techniques, stands at the forefront of liveliness check AI models in the industry.

2. Elevating Customer Experience:

Customer experience is paramount, with 80% of customers considering a brand’s experience as crucial as its services. A significant portion expects companies to understand their unique needs. In predominantly manual operating environments, achieving deep customization at scale can be challenging. However, intelligent FinTech changes this landscape. Machine Learning (ML) models can now study transactions and evolve by learning customer behavioral patterns, enabling personalized financial service delivery.

AI and ML play vital roles in various areas, including sentiment analysis, support quality assessment, intelligent task automation, and client communication. Notably, the Royal Bank of Scotland exemplifies this by automating customer service operations using AI, reducing the burden on front desk teams and enhancing customer service. Additionally, Fannie Mae, a leading mortgage financier in the US, introduced AskPoli, a conversational AI tool capable of understanding complex customer queries and providing accurate responses.

3. Insights Driven by Data:

Decision-making in the financial services sector demands the generation of actionable insights from extensive datasets, a process further complicated by factors like market volatility and valuation risks. Intelligent FinTech equipped with AI capabilities emerges as a game-changer, allowing for the rapid analysis of petabytes of data. Merrill, the investment management arm of Bank of America, exemplifies this by incorporating AI capabilities into its due diligence platform, DatasiteOne. This not only expedites the due diligence process but also provides ready-to-consume insights for informed decision-making in M&A deals.

4. Streamlined Account Reconciliation:

The process of account reconciliation, integral to financial closure, is undeniably laborious yet indispensable. According to EY estimates, a staggering 59% of a finance department’s resources are consumed in managing these transaction-intensive tasks. What’s even more astounding is that a colossal 95% of these efforts are directed towards transactions that are already matched, leaving only the anomalies deserving attention. Manual handling of account reconciliation through rule-based approaches is not only time-consuming but also prone to errors.

FinTech systems that operates at a heightened level of data interpretation, automatically correlating transactions with confidence and learning from feedback to simplify this intricate task. A testament to this efficiency is seen in the case of Federal Bank, which embraced an intelligent Robotic Process Automation (RPA) solution to navigate compliance deadlines. This strategic move not only allowed the bank to meet its targets within half the time but also eradicated instances of account reconciliation errors.

5. Personalized Financial Advisory in Context:

With the global investment landscape growing increasingly intricate, human wealth advisors are finding it challenging to cater to the diverse needs of their clients. In response, the rise of Robo-advisors, intelligent FinTech platforms, comes to the forefront. These platforms autonomously manage accounts, factoring in crucial elements such as a client’s age, risk tolerance, and investment timeline. Today’s intelligent Robo-advisors seamlessly handle the entire spectrum of investment needs, from market analysis to the acquisition and divestment of assets.

The ascendance of Robo-advisory services is palpable, with the global market projected to exhibit a substantial Compound Annual Growth Rate (CAGR) of 31% between 2020 and 2027. A notable player in this domain is Wealthify, a FinTech platform that crafts personalized investment plans for clients and intelligently manages them. Fueled by an AI engine, the platform not only studies but also anticipates shifts in market conditions, proactively recommending portfolio adjustments to clients through push messages. This amalgamation of intelligence and automation underscores the transformative power of FinTech in the realm of personalized financial advisory services.

6. Advancing Inclusivity in FinTech:

In the expansive realm of Financial Technology (FinTech), where services have witnessed unprecedented augmentation, a critical imperative emerges — the necessity for absolute impartiality. Despite its transformative potential, FinTech must transcend biases to ensure impartial decisions and equitable access. Regrettably, a 2021 Federal Reserve study exposed a disconcerting truth — algorithm-driven platforms employed by certain mortgage underwriters had the potential to levy higher rates on borrowers hailing from minority and disadvantaged communities. Such instances, regrettably, are becoming more prevalent, underscoring the urgency to guarantee the social acceptability of FinTech systems. Responding to this imperative, AI-powered toolkits, exemplified by IBM AI Fairness 360 and Google What-if, come to the forefront, offering Financial Services companies the means to conduct comprehensive audits and eradicate programmed biases from the algorithms steering their FinTech operations.

7. Precision in Insurance Underwriting:

The underwriting facet stands as a linchpin in the insurance value chain, yet manual underwriting grapples with sluggishness and susceptibility to errors. The intricacies of considering numerous contingency factors, future uncertainties, and market volatility make the process daunting. A 2022 study unsettlingly reveals a potential risk of $170 billion over the next five years, coupled with customer dissatisfaction in the claims process. Adding to the concern is the fact that underwriters presently allocate 40% of their time to non-core activities, posing a substantial financial risk amounting to nearly $170 billion over the ensuing half-decade.

AI and Machine Learning (ML) emerge as a beacon of hope for insurers and their clientele. A case in point is AXA, which employs predictive underwriting models fueled by ML to automate the pricing of standard risks. This strategic integration not only streamlines the efforts of underwriters but also enhances the speed and accuracy of the process, ultimately contributing to heightened customer satisfaction.

8. Elevated Credit Risk Profiling:

Informed lending decisions hinge on comprehensive credit risk profiling. However, conventional, rule-based credit decisioning practices face challenges in keeping pace with the evolving landscape, characterized by a new generation of borrowers lacking credit history and an exponential surge in diverse datasets. Enter AI-powered credit evaluation tools, capable of swiftly determining credit ratings by assimilating an extensive array of datasets, spanning financial and non-financial realms. A case in point is Perfios AI, employing a robust data analytics platform to intersect and analyze financial, transactional, demographic, economic, and digital datasets for multidimensional customer profiling. Consequently, lenders benefit from transparent and accurate credit decisions, fostering greater confidence in their lending processes.

9. Combatting Money Laundering Challenges:

Money laundering and terrorist financing pose persistent threats to global financial systems. Despite the rapid evolution of Anti Money Laundering (AML) regulations, challenges persist due to the lack of public datasets and reliance on rule-based systems, leading to an unacceptably high number of false positives. A recent study indicates that 41% of Financial Services businesses currently lack the necessary tools for effective AML and sanctions compliance.

To address this, the deployment of Artificial Neural Networks and ML algorithms proves instrumental. These technologies empower investigators to correlate and analyze suspicious financial transaction patterns. For instance, Swedbank, a Stockholm-based banking group, strategically integrated Generative Adversarial Networks (GANs), an advanced AI deep learning technique, into its fraud and AML strategy. This innovative approach resulted in a remarkable 99% reduction in false-positive cases and accelerated the investigation cycle by 50% within a five-year period.

10. Elevating Customer Acquisition:

In the competitive landscape of Financial Services, institutions must attune themselves to customer needs and tailor value propositions for continued relevance. According to the Global State of Customer Experience (CX) in Financial Services, 79% of Financial Services customers are willing to spend more for enhanced convenience.

To meet these expectations, AI-based sentiment analysis systems emerge as invaluable tools for Financial Services institutions. These systems actively listen to and interpret customer feedback across various channels, utilizing Natural Language Processing (NLP) and ML to assess customer opinions regarding products and services. This sophisticated approach enables banks to enhance their understanding of customer preferences and deliver more personalized and convenient services.

Conclusion

While the enumeration above is expansive, it merely scratches the surface of the potential synergy between Artificial Intelligence (AI) and FinTech in driving value within Financial Services. The possibilities are as vast as the horizons of imagination allow. Nevertheless, the realization of this potential hinges significantly on the leadership of Financial Institutions and the receptiveness of their organizational culture to embrace such innovations in both form and substance. Only then can the transformations be meaningful, transcending superficial changes to foster a genuinely progressive FinTech landscape.