We've scoped out these real-world AI use cases so we could detail how artificial intelligence has been a game-changer for FinTech. Few verticals are such a perfect match for the improved capabilities brought by the AI revolution like the financial sector.
Traditional financial services have always struggled with massive volumes of records that need to be handled with maximum accuracy.
However, before the advent of AI and the rise of Fintech companies, very few giants of this industry had the bandwidth to deal with the inherently quantitative nature of this world. (Read Fintech’s Future: AI and Digital Assets in Financial Institutions.)
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Computer Vision and Bank Surveillance
According to the FBI, in the United States Federal Reserve system banks alone are targeted by nearly 3,000 robberies every year. Computer vision-based applications can be used to enhance the security and surveillance systems implemented in all those places and vehicles where a lot of money is stashed (banks, credit unions, armored carriers, etc.).
One example is Chooch AI, which used to monitor sites, entries, exists, actions of people, and vehicles. Visual AI is better than human eye to capture small details such as license plates and is able to recognize human faces, intruders and animal entering the site.
It can even raise a red flag whenever unidentified people or vehicles are present for a suspicious time within a certain space.
Easing the Account Reconciliation Process
Account reconciliation is a major pain point in the financial close process. Virtually every business must face some level of account reconciliation challenge since it’s an overly tedious and complex process that must be handled via manual or Excel based processes.
Because of this, errors are way too common even when this problem is dealt with rule-based approaches. In fact, other than being extremely expensive to set up due to complicated system integration and coding, they tend to break when the data changes or new use cases are introduced and need on-going maintenance.
SigmaIQ developed its own reconciliation engine built on machine learning. The system is able to understand data at a much higher level, allowing for a greater degree of confidence in matching, and is able to learn from feedback.
As humans “teach” the system what is a match and what is not, the AI will learn and improve its performance over time, eliminating the need to pre-process data, add classifications, or update the system when data changes.
Automated Bookkeeping Systems
Small business owners are often distracted by the drudgery of the back-office — an endless series of chores which take away a lot of valuable business time. AI-powered automated bookkeeping solutions such as the ones created by ScaleFactor or Botkeeper are able to assist SMB owners in back-office tasks, from accounting to managing payrolls.
Using a combination of ML and custom rules, processes, and calculations, the system can combine various data sources to identify transaction patterns and categorize expenses automatically. JP Morgan Chase is also employing its own Robotic Process Automation (RPA) to automate all kind of repetitive tasks such as extracting data, capturing documents, comply with regulations, and speed up the cash management process.
Although the first “Automated Trading Systems” (ATSs) trace their history back to the 1970’s, algorithmic trading has now reached new heights thanks to the evolution of the newer AI systems.
In fact, other than just implementing a set of fixed rules to trade on the global markets, modern ATSs can learn data structure via machine learning and deep learning, and calibrate their future decisions accordingly.
Their predicting power is becoming more accurate each day, with most hedge funds and financial institutions such as Numerai and JP Morgan keeping their proprietary systems undisclosed for obvious reasons.
ATSs are used in high-frequency trading (HFT), a subset of algorithmic trading that generates millions of trades in a day. Sentient Technologies’ ATS, for example, is able to reduce 1,800 days of trading to just a few minutes. Other than for their speed, they are appreciated for their ability to perform trades at the best prices possible, and near-zero risk of committing the errors made by humans under psychological pressure.
Their presence on the global markets is pervasive to say the least. It has been estimated that nowadays, computers generate 50-70% of equity market trades, 60% of futures trades and 50% of Treasuries. Automated trading is also starting to move beyond HFT arbitrage and into more complex strategic investment methodologies.
For example, adaptive trading is used for rapid financial market analysis and reaction since machines can quickly elaborate financial data, establish a trading strategy and act upon the analysis in real-time.
Predictive Analytics and the Future of Forecasting
Accurate cash forecasting are particularly important for treasury professionals to properly fund their distribution accounts, make timely decisions for borrowing or investing, maintain target balances, and satisfy all regulatory requirements. However, a 100% accurate forecasting is a mirage when data from internal ERPs is so complicate to standardize, centralize, and digitize — let alone extract some meaningful insight from it.
Even the most skilled human professional can’t forecast outside factors and can hardly take into consideration the myriad of variables required for a perfect correlation and regression analysis.
Predictive analytics applies ML, data mining and modeling to historical and real-time quantitative techniques to predict future events and enhance cash forecast. AI is able to pick hidden patterns that humans can’t recognize, such as repetitions in the attributes of the payments that consist of just random sequences of numbers and letters.
The most advanced programs such as the ones employed by Actualize Consulting will use business trends to pull valuable insights, optimize business models, and forecast a company’s activity.
Others such as the one deployed by Complete Intelligence cut the error rate to less than 5-10% from 20–30%.
Detecting Signs of Discrimination and Harassment
Strongman and sexist power dynamics still exist in financial services, especially since it’s an industry dominated prevalently by males. While awareness has increased, 40% of people who filed discrimination complaints with the EEOC reported that they were retaliated against, meaning that the vast majority of those who are victimized are simply too scared to blow the whistle.
AI can provide a solution by understanding subtle patterns of condescending language, or other signals that suggest harassment, victimization, and intimidation within the communication flows of an organization.
Receptiviti is a new platform that can be integrated with a company’s email and messaging systems to analyze language that may contain traces of toxic behaviors. Algorithms have been instructed with decades of research into language and psychology that analyze how humans subconsciously leak information about their cognitive states, levels of stress, fatigue, and burnout.
A fully automated system, no human will ever read the data to preserve full anonymity and privacy.
In the financial sector, AI can serve a multitude of different purposes, including all those use cases we already mentioned in our paper about the insurance industry. AI and ML are incredibly helpful to ease many cumbersome operations, improve customer experience, and even help employees understand what a customer will most-likely be calling about prior to ever picking up the phone.
These technologies can either substitute many human professionals by automating the most menial and repetitive tasks, or assist them with forecasts and market predictions.
In any case, they are already spearheading innovation in this vertical with the trailblazing changes they keep bringing every day.