Artificial Intelligence (AI) and its practical applications are no longer a distant vision, secluded within the pages of science fiction. Instead, the proliferation of data, the affordability of computing capability, and the availability of greater networking bandwidth have allowed AI to emerge as the pivot in today’s digital economy. The technology is making inroads into almost every sector, albeit with varying degrees of maturity.

Among them, few offered the right ambiance for an almost instant adoption of AI, like Financial Services. Conventionally burdened by high workloads, strict compliance, and ever-growing customer demand for convenience, the industry found a force multiplier in AI-driven FinTech.

Making FinTech Intelligent

But is this surge merely hype, or does AI truly possess the potential to make FinTech more efficient and empathetic by touching upon almost every aspect of Financial Services delivery? Read about the following real-world use cases where intelligent FinTech is already making an impact and judge yourself:

1. Fraud Detection: The Financial Services sector is one of the biggest targets for fraud and cyber intrusions worldwide, with the progressive digitalization of the value chain only expanding the attack surface. In 2021, TransUnion reported a 150% rise in fraud attempts against Financial Services companies, and the trend continues unabated. Alarmingly, malicious actors continue to arm up faster than Financial Services can respond manually. It is where intelligent FinTech helps in taking the fight back to the scammers!

AI allows FinTech to correlate, analyze and interpret millions of data points within seconds, revealing anomalies, irregularities, and suspicious behavior in transactions at a scale unimaginable for human operators. Also, with AI, it becomes possible to identify specific triggers and patterns, preempting fraud attempts automatically.

For instance, identity theft is a plausible threat for both Financial Services businesses and their customers, causing billions in losses for the industry every year. In response, Perfios, a leader in FinTech, has pioneered an AI-based solution for improving face detection, matching, and passive liveness checks during video KYC. It ensures that a Financial Services business deals with an actual person via a live session, not a pre-recorded video or photograph while onboarding. While operating the best liveliness check AI model in the industry, Perfios’ solution also includes advanced augmentation techniques for handling complicated cases of rotated faces and unique facial attributes.

2. Improving Customer Experience: According to Salesforce research, 80% of the customers believe that the brand’s experience is as necessary as their services, with 66% expecting the companies to understand their unique needs. While deep customization at scale can be a nightmare in predominantly manual operating environments, with intelligent FinTech, it is possible. Today Machine Learning (ML) models can study transactions and evolve by learning customer behavioral patterns, helping to personalize Financial Service delivery.

But that’s not all. In fact, AI and ML can be brought into play in several areas, from sentiment analysis, support quality assessment, and intelligent task automation to client communication, helping Financial Services businesses to be truly customer-centric. For instance, the Royal Bank of Scotland automated its customer service operations using AI, exceptionally reducing the burden on its front desk teams and allowing them to serve the customers better. Further, Fannie Mae, the Mortgage Financing leader in the US, developed AskPoli. It is a conversational AI tool that can accurately understand the context and respond to complex customer queries.

3. Data-driven Insights: Decision-making is a resource-intensive process where actionable insights must be churned out by correlating enormous volumes of data. This job becomes infinitely more complex in a high-stakes sector like Financial Services that is influenced by market volatility, fiscal turmoil, and valuation risks. Consequently, FinTech driving decision-making for Financial Service leaders must be intelligent enough to handle robust exposure to diverse datasets and analytical parameters.

It is where AI makes a difference, allowing FinTech to crunch petabytes of data at a lightning pace intuitively. For instance, Merrill, the Investment management arm of Bank of America, has recently equipped its due diligence platform DatasiteOne with AI capabilities. It has paced up the due diligence process while providing ready-to-consume insights for making M&A deals.

4. Accurate Insurance Underwriting: Underwriting is a vital component of the insurance value chain. However, manual underwriting can be extremely slow and error-prone, considering the number of contingency factors, future uncertainties, and market volatility that underwrites must integrate into their assessments. A 2022 study revealed that $170 billion is at risk over the next five years, with customers not being fully satisfied by the claims processes. More worryingly, underwriters today are spending 40% of their time on non-core activities, risking a loss of nearly $170 billion over the next half a decade.

It is where AI and Machine Learning bring respite for insurers and their customers. For instance, AXA has leveraged predictive underwriting models powered by ML to automate the pricing of standard risks. While reducing the time and effort spent by the underwriters, the approach has improved the speed and accuracy of the process, adding to customer delight.

5. Better Credit Risk Profiling: In-depth credit risk profiling is essential to make informed lending decisions. However, with a new generation of borrowers with no credit history and an exponential rise in the number of datasets, rule-based, traditional credit decisioning practices are struggling to keep pace. Here, AI-powered credit evaluation tools can quickly arrive at credit ratings by integrating a wide array of data sets, both financial and non-financial. For example, Perfios AI uses a robust data analytics platform to intersect and analyze financial, transactional, demographic, economic, and digital data sets for multidimensional profiling of a customer. Consequently, it helps the lender to arrive at transparent and accurate credit decisions with greater confidence.

6. Countering Money Laundering: Money laundering and terrorist financing are persistent threats to financial systems worldwide. While Anti Money Laundering (AML) regulations are evolving with speed, the lack of public datasets and dependency on rule-based systems continues to pose a challenge for their enforcement, often leading to an unacceptably high number of false positives. This recent study reports that 41% of the Financial Services businesses are currently ill-equipped to meet AML and sanctions compliance.

Here, Artificial Neural Networks and ML algorithms can be effective, helping investigators to co-relate and study suspicious financial transaction patterns. For instance, Swedbank, the Stockholm-based banking group, employed Generative Adversarial Networks (GANs), an AI deep learning technique, as part of its fraud and AML strategy. The approach reduced false-positive cases by 99% and accelerated the investigation cycle by 50% within five years.

7. Improved Customer Acquisition: Like any other business, Financial Services institutions need to listen to their customers and shape their value propositions accordingly to stay competitive. The Global State of CX in Financial Services revealed that 79% of Financial Services customers are ready to spend more for convenience.

Here, AI-based sentiment analysis systems can help banks listen and deliver better on their customers’ needs by gathering and interpreting behavioral intelligence at scale. Such systems continuously ingest activities across feedback channels and use Natural Language Processing (NLP) and ML to gauge customer opinions about products and services.

For instance, Atom Bank, the first digital-only challenger bank to be granted a full regulatory license in the UK, extensively leveraged AI for its Voice of Customer (VoC) program. In-depth sentiment analysis using structured and unstructured data gathered through multiple feedback channels allowed Atom Bank to discover what mattered most to its customers. Delivering accordingly, the business surged its Trustpilot score and became a customer favorite!

8. Simplified Account Reconciliation: Account reconciliation is a tedious but essential part of financial closure. EY estimated that up to 59% of a finance department’s resources are consumed in managing such transaction-intensive tasks, and a whopping 95% of the efforts are wasted on transactions that are already matched, while in fact, only the anomalies call for attention. Further account reconciliation can be notoriously error-prone as it is mainly handled manually through rule-based approaches.

Here an intelligent FinTech system that can interpret data at a higher level, automatically co-relate transactions with confidence, and learn from feedback can simplify the job. Federal Bank realized this when it embraced an intelligent RPA solution to help it meet compliance deadlines. It allowed the bank to achieve its targets within half the time while eliminating the cases of account reconciliation errors.

9. Contextualized Financial Advisory: As the global investment landscape becomes complex, it is increasingly becoming difficult for human wealth advisors to cater to the diverse needs of their clients. In response, a Robo-advisor is a FinTech platform that can auto-manage an account considering a client’s age, risk tolerance, and investment timeline. Today intelligent Robo-advisors can handle all investment needs, from analyzing markets to purchasing and disposal of assets.

No wonder Robo-advisory services are gaining steam, with the global market poised to exhibit a handsome CAGR of 31% between 2020 and 2027. One among them is Wealthify which builds personal investment plans for clients and manages them intelligently. The FinTech platform is powered by an AI engine that studies and anticipates shifts in market conditions, recommending adjustments in the portfolio to clients through push messages.

10. Inclusive FinTech: While FinTech has augmented the scope of Financial Services delivery like never before, it must be free from bias to enable fair decisions and equitable access. Unfortunately, a Federal Reserve study in 2021 revealed that algorithm-driven platforms developed by some mortgage underwriters could charge a higher rate for borrowers from minority and disadvantaged communities. Such cases are being reported more often than not and clearly there is a need to ensure the social acceptability of FinTech systems. In response, AI-enabled toolkits like IBM AI Fairness 360 and Google What-if can help Financial Services companies to audit and eliminate programmed bias from algorithms driving their FinTech operations.

Conclusion

While the above list is indeed long, it is not exhaustive. The possibilities of how AI can complement FinTech, allowing Financial Services to drive value, are only limited by imagination. However, much will depend upon the leadership of a Financial Institution and the permeability of its culture to adopt such innovations in letter and spirit and ensure that the changes are not merely skin deep.