Banks alone are expected to spend $5.6 billion USD on AI and Machine Learning (ML) solutions in 2019 — just a fraction of what they’re expecting to earn since the profits generated may reach up to $250 billion USD in value.

From automating the most menial and repetitive tasks to free up the time to focus on higher level objectives, to assisting with customer service management and reducing the risk of frauds, AI is employed from back-office tasks to the frontend with nimbleness and agility.

1. Fraud Detection and Compliance

According to the Alan Turing Institute, with $70 billion USD spent by banks on compliance each year just in the U.S., the amount of money spent on fraud is staggering. And when the number of reported cases of payments-related fraud has increased by 66% between 2015 and 2016 in the United Kingdom, it’s clear how this problem is much more than a momentary phenomenon.

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AI is a groundbreaking technology in the battle against financial fraud. ML algorithms are able to analyze millions of data points in a matter of seconds to identify anomalous transactional patterns. Once these suspicious activities are isolated, it’s easy to determine whether they were just mistakes that somehow made it through the approval workflow or traces of a fraudulent activity.

Mastercard launched its newest Decision Intelligence (DI) technology to analyze historical payments data from each customer to detect and prevent credit card fraud in real time. Companies such as Data Advisor are employing AI to detect a new form of cybercrime based on exploiting the sign-up bonuses associated with new credit card accounts.

Even the Chinese giant Alibaba employed its own AI-based fraud detection system in the form of a customer chatbot — Alipay.

2. Improving Customer Support

Other than health, no other area is more sensitive than people’s financial well-being. A critical, but often overlooked, application of AI in the finance industry is customer service. Chatbots are already a dominating force in nearly all other verticals, and are already starting to gain some ground in the world of banking services, as well. (Read We Asked IT Pros How Enterprises Will Use Chatbots in the Future. Here’s What They Said.)

Companies like Kasisto, for example, built a new conversational AI that is specialized in answering customer questions about their current balance, past expenses, and personal savings. In 2017, Alibaba’s Ant Financial’s chatbot system reported to exceed human performance in customer satisfaction.

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Alipay's AI-based customer service handles 2 million to 3 million user queries per day. As of 2018, the system completed five rounds of queries in one second.

Other companies, such as Tryg, used conversational AI techs such as boost.ai to provide the right resolutive answer to 97% of all internal chat queries. Tryg’s own conversational AI, Rosa, works as an incredibly efficient virtual agent that substitutes inexperienced employees with her expert advice.

Virtual agents are able to streamline internal operations by amplifying the capacity and quality of traditional outbound customer support. For example LogMeIn’s Bold360 was instrumental in reducing the burden of the Royal Bank of Scotland’s over 30,000 customer service agents customer service who had to ask between 650,000 and 700,000 questions every month.

The same company also developed the AI-powered tool AskPoli to answer all the challenging and complex questions asked by Fannie Mae’s customers.

3. Preventing Account Takeovers

As a huge portion of our private identity has now become somewhat public, in the last two decades cybercriminals have learned many new ways to use counterfeit or steal private data to access other people’s accounts.

Account Takeovers (ATOs) account for at least $4 billion USD in losses every year, with nearly 40% of all frauds occurred in 2018 in the e-commerce sector being due to identity thefts and false digital identities.

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Smartphones appear to be the weakest link in the chain in terms of security, so the number of mobile phone ATO incidents rose by 180% from 2017 to 2018.

New AI-powered platforms have been created such as the DataVisor Global Intelligence Network (GIN) to prevent these cyber threats, ranging from social engineering, password spraying, and credential stuffing, to plain phone hijacking.

This platform is able to collect and aggregate enormous amounts of data including IP addresses, geographic locations, email domains, mobile device types, operating systems, browser agents, phone prefixes, and more collected from a global database of over 4 billion users.

Once digested, this massive dataset is analyzed to detect any suspicious activity, and then prevent or remediate account takeovers.

4. Next-gen Due Diligence Process

Mergers and acquisitions (M&A) due diligence is a cumbersome and intensive process, requiring a huge workload, enormous volumes of paper documents, and large physical rooms to store the data. Today the scope of due diligence is now even broader, encompassing IT, HR, intellectual property, tax information, regulatory issues, and much more.

AI and ML are revolutionizing it to overcome all these difficulties.

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Merrill has recently implemented these smart technologies in its due diligence platform DatasiteOne to redact documents and halve the time required for this task. Data rooms have been virtualized, paper documents have been substituted with digital content libraries, and advanced analytics is saving dealmakers' precious time by streamlining the whole process.

5. Fighting Against Money Laundering

Detecting previously unknown money laundering and terrorist financing schemes is one of the biggest challenges faced by banks across the world. The most sophisticated financial crime patterns are stealthy enough to get over the rigid conventional rules-based systems employed by many financial institutions.

The lack of public datasets that are large enough to make reliable predictions makes fighting against money laundering even more complicated, and the number of false positive results is unacceptably high.

Artificial neural networks (ANN) and ML algorithms consistently outperform any traditional statistic method in detecting suspicious events. The company ThetaRay used advanced unsupervised ML algorithms in tandem with big data analytics to analyze multiple data sources, such as current customer behavior vs. historical behavior.

Eventually, their technology was able to detect the most sophisticated money laundering and terrorist financing pattern, which included transfers from tax-havens countries, abnormal cash deposits in high risk countries, and multiple accounts controlled by common beneficiaries used to hide cash transfers.

6. Data-Driven Client Acquisition

Just like in any other sector where several players fight to sell their services to the same customer base, competition exists even among banks. Efficient marketing campaigns are vital to acquire new clients, and AI-powered tools may assist through behavioral intelligence to acquire new clients.

Continuously learning AI can digest new scientific research, news, and global information to ascertain public sentiment and understand drivers of churn and customer acquisition.

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Companies such as SparkBeyond can classify customer wallets into micro-segments to establish finely-tuned marketing campaigns and provide AI-driven insights on the next best offers.

Others such as LelexPrime make full use of behavioral science technology to decode the fundamental laws that govern human behaviors. Then, the AI provide the advice required to make sure that a bank’s products, marketing and communications align best with their consumer base's needs.

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