Complete Intelligence

QuickHit Visual (Videos)

QuickHit: How healthy are banks in this COVID-19 era?


This week’s QuickHit episode, Tony Nash talked with Dave Mayo, CEO and Founder of FedFis, and an expert on banking, finance, and Fintech. This episode looks at US financial institutions like banks and how they are faring during the Coronavirus pandemic. Will new financial technologies help streamline the process of providing services like loans to medium and small businesses?


Watch the previous QuickHit episode on the Status of Global Supply Chain in Time of Coronavirus with the president of Secure Global Logistics, George Booth.

The views and opinions expressed in this QuickHit episode are those of the guests and do not necessarily reflect the official policy or position of Complete Intelligence. Any content provided by our guests are of their opinion and are not intended to malign any political party, religion, ethnic group, club, organization, company, individual or anyone or anything.

Show Notes


TN: Hi, everybody. This is Tony Nash. I’m the founder and CEO of Complete Intelligence. This is our Quick Hit where we talk to industry experts about issues in markets and in industries.


Today we’re with Dave Mayo. Dave is the founder and CEO of FedFis based in Texas. Dave, thanks for joining us, I really appreciate it.


Can you tell us a little bit about FedFis? And then I’d really love to jump into how you’re helping out the financial services sector.


DM: Sure. We’re a unique company. We sit as a layer above banking, we call FI fintech, and then fintech. From the banking side obviously, we are a data company and provider and intelligence. From the FI fintech side, those would be the vendors to the institutions like their core mobile offering. And then FinTech, that’s the new stuff, right? That’s the sexy stuff, like the Chime and the SoFis and those types of companies that used to be alt banking and now they’re joined back to banking again. So we help all of those different layers in one way or another through a data set that we have and intelligence.


TN: With everything going on in the wake of Coronavirus, there’s been a lot of talk about fiscal stimulus coming out of D.C. and stimulus through the Fed and other things. What is the health of the banking sector from your perspective? Because back in 2008 the banking sector was the worry, right? Is that the worry now? Is that something we should be worried about?


DM: I think our banking industry is based on a level of faith. It always has been, right? Now that said, this is a completely different situation. Banks are very well-capitalized. Banks are not the cause of the problem. We don’t have a systemic banking problem or issue. We’re very, very healthy right now. When you talk about a stimulus being put into the economy, the more money flows in and out, the more people spend and buy and purchase, the better things are. That’s just the way the banking industry is built.


TN: How do you see banking and FinTech really helping? Obviously we know how they help big companies with big placements and debt and these sorts of things. But how do you see them helping small and mid-sized companies with this economic gulf that we have right now, where the economy’s effectively been turned off for a period of time, which is a bit weird? How do you see, what you’re doing, and banks generally, really helping out there on the smaller and midsize level?


DM: I think there’s a big gap in education in our country when it comes to banking. People are like, “I don’t like banks” or “I like banks.” When there are the big banks, the big four: the B of A, Wells Fargo, Chase, Citi. And then we have community banks.


Community bankers all across the country, they’re the life of our banking system. They’re the heartbeat. It’s actually a lower touch point for consumers and FinTech with the dramatic decline in a number of community institutions that has really opened up this opportunity for a FinTech. And the reason being is it’s a direct touch point.


So if you were to say “I want to use my mobile device” or “I want to use my online to do banking without having to actually drive to an institution and deal with all their policies and all of the things that go with it,” it’s a faster connection point. And I think we’re probably going to see a lot of that in these business loans the PPP loans through the stimulus plan.


TN: How do we actually execute that from the Treasury to the small business owner or to the individual that needs help? So, do you think that some of these FinTechs are kind of non-banks? I mean, would you consider them kind of non-banks within this system? Do you think they’ll be able to do this stuff faster? And I don’t mean this as a negative to banks. Banks are highly regulated. Do you think some of these FinTechs will be able to do some of this stuff faster?


DM: It depends on which way you look at it. Because here’s the deal: so when we talk about banking and then we talked about FI FinTech and then FinTech. So a bank is a chartered institution and FI FinTech is a technology arm of that like online banking, mobile banking. A FinTech is something that looks like a bank, talks like a bank, but it doesn’t have a charter. It’s not really a bank. So they have to partner with an existing bank to charter. So there’s a bank behind every FinTech company. So when you think of Chime and companies like that, there’s an actual bank behind that company that’s doing the regulatory side of this to protect consumers.


TN: You guys track a lot of data around banking and real estate and consumer stuff and industry stuff. Are you seeing any data that’s really talking about or raising your worries about the velocity of money about how quickly people are spending? Are you seeing that data? If it’s worrying you, when does that worry end for you? Do you see us going back in to say a quasi-normal situation within the next two months or something?


DM: Predicting the future I’ve never really been a big proponent of. That’s your business. But for us, what we look at are key components.


One way to measure things right now is to look at a mortgage note on a 15 or a 30. What is the spread between, what we would call in the old days, prime and what is the asking rate on that loan So you’re generally looking at above 3 percent. And as long as you’re looking at that, that’s a strong indication that there’s a lot of refis going on right now. And so the spread is there. That’s an adequate spread for banks to make money. There’s a huge volume of it going on. And as long as we see that volume and people continue to go to the bank, cash their checks, direct deposit always helps.


When we use our debit cards, when we go out and do the things that we do. Changing our mechanism of spending money whether it’s through Amazon as opposed to going through the mall doesn’t change the fact that you’re still spending money. Those are all positive things.


But I think the one thing we want to keep an eye on is the volume of lending. Everyone in a situation like this is going to have a tendency to kind of climb up a little bit. And, as long as that continues to flow, and one of the primary things that I’d be looking at is refis and other lending types of loan, etc.


TN: Are there any specific indicators you’re looking at on the commercial side to see if people are climbing up?


DM: I don’t really see anything from that perspective. I don’t think people are running out there right now at a time like this. It’s fairly obvious. You wouldn’t want to run out and start a new construction project or something like that. Those are gonna have an impact. There’s no way around it, but there again that’s what stimulus is there to offset.


Right now, I would say we’ve got a very healthy banking system. We’re coming out of a very healthy economy and so what’s our time frame of a bounce-back is it going to be a v-bottom or is it going to be spread out? I think it’ll be a little more spread out than a V-bottom and I think they’ll probably be multiple cycles of this that go on to some degree.


But starting from a really healthy position in our banking system and in our economy, this will pass. And when it does, here’s the thing I think is so interesting, unprecedented levels of stimulus and, the old saying you don’t fight the Fed, right? So does the market go up and we have a stimulated economy? Of course it does. And with this level of pent-up demand and stimulus, will there be a bounce back? Yeah, there’ll be a bounce back. The question is how huge will it be and how fast?


TN: That’s great Dave. It’s a huge source of optimism. Thank you so much for that and I really appreciate that you’ve taken the time to join us today. So really appreciate your time and and thanks very much for, for all that you’ve shared with us today. I really appreciate it.

News Articles

Top 12 AI Use Cases: Artificial Intelligence in FinTech

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.



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.


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.


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 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.


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.


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.


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.



7. 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.



8. 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.



9. 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.



10. Algorithmic Trading


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.



11. Predictive Intelligence 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. It’s clearly a financial forecasting challenge.


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 intelligence 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 reduce error rate to less than 5-10% from 20–30%.



12. 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.



Final Thoughts


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.



Written by Claudio Buttice

Dr. Claudio Butticè, Pharm.D., is a former clinical and hospital pharmacist who worked for several public hospitals in Italy, as well as for the humanitarian NGO Emergency. He is now an accomplished book author who has written on topics such as medicine, technology, world poverty, and science. His latest book is “Universal Health Care” (Greenwood Publishing, 2019).

A data analyst and freelance journalist as well, many of his articles have been published in magazines such as CrackedThe ElephantDigital JournalThe Ring of Fire, and Business Insider. Dr. Butticè also published pharmacology and psychology papers on several clinical journals, and works as a medical consultant and advisor for many companies across the globe.

Full Bio


This article first appeared on Techopedia at