15 Examples of AI In Banking | Built In (2024)

Fintech companies and traditional banks are occasionally thought of as being at odds with each other. But lately, AI has been changing that dynamic.

The AI in banking industry is expected to keep growing too, as it’s projected to reach $64.03 billion by 2030.

AI has impacted every banking “office" — front, middle and back. That means even if you know nothing about the way your financial institution uses, say, complex machine learning to fend off money launderers or sift through mountains of data for fraud-related anomalies, you’ve probably at least interacted with its customer service chatbot, which runs on AI.

Read on to learn how else AI is transforming the way banks operate, from investment assistance and consumer lending to credit scoring, smart contracts and more.

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AI in Customer Support Examples

Traditional banks — or at least banks as physical spaces — have been cited as yet another industry that’s dying and some may blame younger generations. Indeed, nearly 40 percent of Millenials don’t use brick-and-mortar banks for anything, according to Insider. But consumer-facing digital banking actually dates back decades, at least to the 1960s, with the arrival of ATMs.

Since then, clients’ customer support expectations haven’t really changed in terms of what they expect, but how they expect them is another story. AI has clearly impacted this landscape, with AI-enabled chatbots and voice assistants now being the norm at major financial institutions. We’re also seeing AI impact biometric authorization and — for those who enjoy the occasional throwback visit to a physical bank — AI-enabled robotic help. Here are a few companies changing how AI impacts banking customer support.

Location: Detroit, Michigan

Ally has been in the banking industry for over 100 years, but has embraced the use of AI in its mobile banking application. The bank’s mobile platform uses a machine-learning-based chatbot to assist customers with questions, transfers and payments as well as providing payment summaries. The chatbot is both text and voice-enabled, meaning users can simply speak or text with the assistant to take care of their banking needs.

Location: McLean, Virginia

Capital One is another example of a bank embracing the use of AI to better serve its customers. In 2017, the bank released Eno, a virtual assistant that users can communicate with through a mobile app, text, email and on a desktop. Eno lets users text questions, receive fraud alerts and takes care of tasks like paying credit cards, tracking account balances, viewing available credit and checking transactions. The AI assistant can communicate like human users do — even using emojis.

Location: New York, New York

Digital-first banks have been making headlines and attracting major investors in certain parts of the globe, especially the U.K. Kasisto is one of the companies that’s brought digital-first banking to the United States.

Kasisto’s conversational AI platform, KAI, allows banks to build their own chatbots and virtual assistants. It’s rooted in AI reasoning and natural-language understanding and generation, which means it can handle sophisticated questions about finance management Kasisto’s platform has been used by banking institutions like the UAE-based digital bank Liv., DBS Bank, Standard Chartered Bank and TD. These banks use KAI-based bots to walk customers through how to make international transfers, block credit card charges and transfer you to human help when the bot hits a wall.

Location: Waltham, Massachusetts

One of the world's most famous robots, Pepper is a chipper humanoid with a tablet strapped to its chest. Debuting in 2014, Pepper didn’t incorporate AI until four years later, when MIT offshoot Affectiva injected it with sophisticated abilities to read emotion and cognitive states. Following that upgrade, HSBC introduced it on bank floors — including the bank’s flagship branch on Fifth Avenue in New York. Pepper has since been rolled out at Miami and Beverly Hills locations as well.

Pepper primarily handles hosting duties for HSBC — greeter basics like teaching customers how to open accounts, cracking jokes, relaying credit card details and more.

Location: Fully remote

Biometrics have long since graduated from the realm of sci-fi into real-life security protocol. Chances are, with smartphone fingerprint sensors, one form is sitting right in your hand. At the same time, biometrics like facial and voice recognition are getting increasingly smarter as they intersect with AI, which draws upon huge amounts of data to fine-tune authentication.

The security boons are self-evident, but these innovations have also helped banks with customer service. One notable recent example is NatWest, which became the first major U.K. bank to allow customers to open accounts remotely with a selfie. AI-powered biometrics — developed with software partner HooYu — match in real time an applicant’s selfie to a passport, government-issued I.D. card or other official photo identification document.

Location: Fully remote

Automation hit investment banking earlier than other bank sectors — and it hit hard.

Simudyne is a tech provider that uses agent-based modeling and machine learning to run millions of market scenarios. Its platform allows financial institutions to run stress test analyses and test the waters for market contagion on large scales. The company’s chief executive Justin Lyon told the Financial Times that the simulation helps investment bankers spot so-called tail risks — low-probability, high-impact events.

Simudyne’s technology has been recognized by major banking institutions as Barclays led a $6 million funding round for the fintech company in 2019.

AI in Fraud Protection Examples

While AI hasn’t dramatically reshaped customer-facing functions in banking, it has truly revolutionized so-called middle office functions.

The middle office is where banks manage risk and protect themselves from bad actors. That includes fraud detection, anti-money laundering initiatives and know-your-customer identity verification. And sometimes that means incorporating AI into legacy, rules-based anti-fraud platforms. Here are several companies influencing how AI is used in fraud protection.

Location: New York, New York

“Know your customer” is pretty sound business advice across the board — it’s also a federal law. Introduced under the Patriot Act in 2001, KYC checks comprise a host of identity-verification requirements intended to fend off everything from terrorism funding to drug trafficking. They’re also commonly done in tandem with anti-money laundering efforts.

Socure’s identity verification system, ID+ Platform, uses machine learning and artificial intelligence to analyze an applicant’s online, offline and social data to help clients meet strict KYC conditions. The system runs predictive data science on information such as email addresses, phone numbers, IP addresses and proxies to investigate whether an applicant’s information is being used legitimately.

Location: San Jose, California

Vectra assists financial institutions with its AI-powered cyber-threat detection platform. The platform which automates threat detection, reveals hidden attackers specifically targeting banks, accelerates investigations after incidents and even identifies compromised information.

A Vectra case study provides an overview of its work to help a prominent healthcare group prevent security attacks. Vectra’s platform identified behavior resembling an attacker probing the footprint for weaknesses and disabled the attack.

Location: Jacksonville, Florida

FIS provides a host of banking and financial solutions. The company uses C3 AI in its compliance hub that strives to help capital markets firms fight financial crime as well as in its credit analysis platform. The machine learning-based platform aggregates and analyzes client data across disparate systems to enhance AML and KYC processes. The company’s credit analysis solution uses machine learning to capture and digitize financials as well as delivers near-real-time compliance data and deal-specific characteristics.

Location: Palo Alto, California

Up to $2 trillion is laundered every year — or five percent of global GDP, according to UN estimates. The sheer number of investigations coupled with the complexity of data and reliance on human involvement makes anti-money laundering very difficult work. It’s also expensive. AML compliance costs shot up more than 50 percent between 2015 and 2018.

Ayasdi’s AI-powered AML incorporates three key advancements: intelligent segmentation, or optimizing the data-sifting process to produce the fewest number of false positives; an advanced alert system, which auto-categorizes alert priorities; and advanced transaction monitoring, which uses machine learning to spot suspicious anomalies.

Case in point: Ayasdi’s AML AI was able to process hundreds of data points for Canada’s Scotiabank and for Italian banking group Intesa Sanpaolo, purportedly resulting in a massive drop in false-positive alerts.

Location: Mountain View, California

Even though most banks implement fraud detection protocols, identity theft and fraud still cost American consumers billions of dollars each year.

As cyber-cheats become increasingly sophisticated (manipulating identity information through account takeovers, exploiting cloud server IP addresses), financial institutions look to AI for help. DataVisor’s machine learning uses big data and clustering algorithms in real time to counteract application and transaction fraud. The company says it has helped financial institutions save $15 million in losses and manual review costs.

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AI in Lending and Risk Management Examples

A study published by U.C. Berkeley researchers titled “Consumer-Lending in the FinTech Era” came to a good-news-bad-news conclusion. The good news? Fintech lenders discriminate less than traditional lenders overall by about one-third. The bad news? They still discriminate. So while things are far from perfect, AI holds real promise for more equitable credit underwriting — as long as practitioners remain diligent about fine-tuning the algorithms.

Beyond credit scoring and lending, AI has also influenced the way banks assess and manage risk and how they build and interpret contracts. Here are some companies using AI to improve lending and risk management.

Location: Cambridge, Massachusetts

Kensho, an S&P Global company, provides machine intelligence and data analytics to leading financial institutions like J.P. Morgan, Bank of America and Morgan Stanley.

Kensho’s software offers analytical solutions using a combination of cloud computing and natural language processing, and it can provide easily understandable answers to complex financial questions, as well as quickly extract insights from tables and documents.

Location: Pittsburgh, Pennsylvania

PNC Financial Services Group offers a variety of digital and in-person banking services. That includes the corporate online and mobile banking platform PINACLE, which comes with a cash forecasting feature that uses artificial intelligence and machine learning to make data-based predictions about a company’s financial future in order to inform decision making.

Location: Burbank, California

Redlining, the illegal denial of credit or home loans because of race, stands as one of America’s great shames. But lending practices are often tainted by bias even when explicit discrimination isn’t so apparent, like when high-cost loans notoriously and disproportionately affected minorities during the mortgage crisis. As ZestFinance founder and former Google CIO Douglas Merrill told Forbes, “[Credit] models are by nature very biased. The ability to make decisions that are biased is an epidemic.”

ZestFinance’s AI-based software purportedly generates fairer models, essentially by downgrading credit data that it has “learned” results in unfair decisions, thus lessening the weight of some traditional (but not entirely reliable) metrics like credit scores.

Of course, AI is also susceptible to prejudice, namely machine learning bias, if it goes unmonitored. As Merrill recently said in testimony to the House Financial Services Committee Task Force on Artificial Intelligence, “Lenders put themselves, consumers and the safety and soundness of our financial system at risk if they do not appropriately validate and monitor ML models.

Location: San Mateo, California

In the age of instant payments, the idea of waiting for a purchase to “clear” will one day seem as antiquated as an abacus. Increasingly, consumers expect their accounts to immediately reflect when they've bought something. At the same time, there are cyber criminals working tirelessly to find the newest, most effective way of swiping someone’s identity and sensitive information.

In an attempt to combat this, more and more banks are using AI to improve both speed and security. Take data science company Feedzai, which uses machine learning to help banks manage risk by monitoring transactions and raising red flags when necessary. It has partnered with Citibank, introducing AI technology that watches for suspicious payment behavioral shifts among clients before payments are processed.