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Finance without AI: a word that used to induce anxiety
When we speak about finance, a plethora of concepts pop up into our mind: data analytics, management of transactions, bank account surveillance, one of the pillars of a nation's economy ... along with the "anxiety adjectives" paired to them: obscure, delicate, intricate, fluctuating, prone to inflation, confidential and ... stressful!
But why? This is because for many years, and even so far, data security and integrity have been the main concern in every area where money flows are involved. It seems that money would be the root of all evil.
However, with the birth of financial technology start-ups - aka FinTech, previous issues are mitigated and future risks put at bay.
Using technologies like Artificial Intelligence, FinTechs have found a very sharp trend able to save this state of affairs.
Moreover, delegating complex tasks to AI solves the socio-economic problems associated with all the needs implied by this new era: massive data quantification and processing, mandatory social distancing, exaggerated desire for personalization, daily multitasking, etc.
Each negative connotations mentioned before could now be replaced by the brighter side of them: what was delicate becomes structured, complex is now predictable, fluctuating is monitored, subject to inflation becomes controlled, confidential displays security, while stressful is now replaced by guaranteed.
Let us see how.
How did Artificial Intelligence revolutionize finances via FinTech companies?
By injecting a smart touch into finance, AI made remote transactions "wiser" by facilitating mobile banking and online payrolls within better-governed decisions, just to cite one popular use case.
In this article, let us see what FinTech is about, then consider key FinTech areas (use cases) wherein artificial intelligence brought a dramatic contribution as well as these former's target customers. Afterward, we are going to overview what and how some successful unicorns did, and finally discuss what to expect from the future of FinTech with AI.
- What is FinTech?
- What are Key AI Use cases in FinTech?
- Target customers
- What did successful unicorns do and how?
#1 What is FinTech?
FinTech = Finances + Technology
According to Investopedia, FinTech is not just a fusion of finance and technology, but much more than that. The term is used to point out any innovative start-up that has introduced all sorts of disruptive technologies into the stock market and banking sector with any form of cutting-edge ideas. They are also those firms who are developing systemic solutions and strategies using new technologies to solve socio-economic problems.
FinTech seeks to improve finance by employing automation and the seamlessness of processes without or with a limited intervention of any human being.
The main channels of advancement are the development of intelligent applications and a more effective control of the entry and exit of money.
#2 What are Key AI Use cases in FinTech?
How can artificial intelligence be applied to FinTech?
As the greatest tenets of FinTech can be surrounded in these few words :
- secured credentials,
- decentralized access,
- remote operations
AI can intervene in each of these fields to accelerate the interactions between its nuts and bolts.
The Techopedia website has recensed 12 principal use cases of FinTech comprising:
- Fraud Detection and Compliance: Fraudulent cash transfers have caused large capital losses in businesses. AI helped neutralize it, with powerful algorithms to identify abnormal transactional patterns, suspicious anomalies, and scam traces.
- Improving Customer Support: The quality of customer services is one of the key values of marketing. As a result, the lack of it can cause customers to withdraw. By offering a 24/7 presence, especially with chatbots, AI helped to maintain a better proximity with clients.
- Preventing Account Takeovers: Cybercrime affects many sectors, and finance is not spared. Many have already been the victim of hijacks due to identity falsification. To submit it, AI helps distinguish actual users from forgeries by analyzing the references provided in terms of geographic location and terminal settings: IP addresses, etc.
- Next-gen Due Diligence Process: Mergers and acquisitions (M&A) due diligence includes all kinds of tasks requiring the processing of large information contained in disparate files and references as various as Human resources and intellectual property credits.
- Fighting Against Money Laundering: Artificial neural networks(ANN) and ML algorithms process massive data to check stealth money threats by cyber pirates.
- Data-Driven Client Acquisition: Banks just like any other field of the market are a battlefield of fierce competition. As a growth-marketing strategy, AI is used to build systems of data analytics for aiming big at the right prospects with clustering and recommendation engines.
- Computer Vision and Bank Surveillance: Banks and automatic cash dispensers are often the target of robberies. With these AI technologies, cameras can pick up details as subtle as the license plate number and detect the suspects' silhouette and duration of lingering around the place.
- Easing the Account Reconciliation Process: It is a strenuous task to compile lines and lines of data entries over time which also render errors way too common. It is because apart from recording losses it also consumes high computational cost.
- Automated Bookkeeping Systems: By releasing more time, Robotic Process Automation(RPA) helps corporates to focus on higher-value tasks.
- Algorithmic Trading: “Automated Trading Systems” (ATSs) has been leveled up to handle investment strategies in order to counsel the third-parties.
- Predictive Analytics and the Future of Forecasting: Predictive analytics utilizes data mining and modeling along with correlation and regression analysis to design business strategies with deeper lucidity.
- Detecting Signs of Discrimination and Harassment: Chauvinistic attitudes can be detected even in finance as reported by the EEOC. The clues for filing for such toxic behavioral misdeeds can be tracked through mails and other means of written supports through sentiment analysis.
#3 Target customers
What MarketPlace does FinTech work for?
According to Investopedia, roughly speaking, we can distinguish 4 main categories of FinTech customers :
1) B2B for banks 2) and B2C for their business clients,
3) B2C for small businesses and 4) B2C for consumers.
Investopedia also notices that FinTech was mainly configured for the generation of Millenials instead of GenXers and Baby Boomers just like most technology, and this despite the fact that they may spark the same interest. This is justified by 4 main reasons :
- their number: they represent the thickest age band in the marketplace
- their increasing income: millennials tend to earn more, both genders tend to be professionally active
- their potential wealth as the heirs of the former generations: they have the potential to become affluent
- and the fact that FinTech actually addresses their issues which it fails to provide to other generations (to a certain extent)
Mobile banking, Big Data, reliable analytics, and decentralized access are pinpointed as the new means of interconnecting the third parties. In consequence, these are all domains where AI finds its niche
#4 What did successful start-up unicorns do and how?
This is the question that exudes curiosity either from ones who look for ways for erecting a FinTech or from those who already have created one but need some kind of guidance to skyrocket their project.
Forbes establishes an annual record of 50 FinTechs (called FinTech 50) that have shown their originality, endeavored and succeeded to pass the triple- criteria of: "ease, speed, and affordability". Here, it is highlighted in which way these firms had leveraged technology to improve finance conditions tangibly.
Speaking of the subject, as last year, a herd of startup unicorns come on the stage each year, seizing the opportunity of jumping on the moving train way before anyone else does.
Following the trail of these success stories, many others are still expected for these upcoming years.
The Built-in website also quotes several promising young FinTech companies that created interesting banking applications.
Some of those have already managed to double their turnover in two years or less- in such a way that they even exceeded the initial investment.
Investing in the finances sure enriches those who dared( and were as lucky as consistent ) launch their ground-breaking ideas at the perfect timing.
This might be a platitude or a cliche, but to get inspired, it might not be foolish to align the origin of a FinTech to a context of the time that made a need prevailing.
Under the circumstance of social distancing, we should admit that among others, mobile money technology has become the absolute means of trade instead of mere cash.
For such an extreme case as the confinement that is now necessary, it is not inappropriate to say that the use of online shopping apps and online payment methods would be somehow salutary. Provided that they are made safe- which they all strive to be.
This is enough to give us a clue to the extreme importance of AI in FinTech.
"What is the secret ingredient of their success?" Let us dig the question a little more seriously.
The feature FinTechs have in common is that they started from the traditional concept of finance, to get to the way of improving and upgrading it thanks to technology. Often, AI embedded into FinTech takes the form of blockchain, chatbots, and other machine learning systems.
The most accomplished ones work to implement targeted solutions of digital finance and banking in response to the request of the 77% of customers for instance, who precisely prefer debit or credit card over cash. And it pays off. And a hundred folds.
Let us just take the example of these thriving startups that found their gold mine recently by designing Robot-advisor applications to counsel investors on where to put their coins.
Thanks to the higher level of automation, FinTechs promote lower fees against the same quality of service just by twisting the regular mechanism that used to tax high while undergoing long procedures. As far as human tasks are concerned, machines are certainly faster and less prone to error which makes them as appealing as they are useful.
Finally, by controlling breaches, blockchain decreased the number of catastrophic cybercrimes successfully in mobile banking, cryptocurrency, investment, and fraud detection. Therefore, investing in a blockchain FinTech also offers a pathway for success in the long term.
On the whole, FinTechs are based on synergizing techno that enables unprecedented financial management.
To summarize, we can affirm that :
- many are the typical issues and challenges in finances that AI solves
- AI is used in FinTech to custom design solutions for challenges encountered in traditional finance: massive data analytics, fraud detection, predictive analytics, decentralization of access, and so on.
- the FinTech50 nominees can be taken as role-models because of their "quick, user-friendly, money-savviness'
- lucrative projects usually involve matters that have been scrutinized through cultural, socio-economic, and psychological lenses.
- AI is an edgy technology. So are FinTechs that use it.
We have dug for you here some of the best niche websites on the matter.
- Blogs on FinTech: Top 100 Fintech Blogs and Websites For Financial Technology Pros in 2020
- AI use cases of AI: Top 12 AI Use Cases: Artificial Intelligence in FinTech
100+ AI Use Cases & Applications in 2020: In-Depth Guide
- Blockchain: The Future of Blockchain
- Biggest firms: The 10 biggest fintech companies in America
- Newcomers: Fintech 50 2019 the Newcomers
- ML FinTech: 10 MACHINE LEARNING COMPANIES IMPROVING THE FINANCE INDUSTRY
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- Reports: Global FinTech Report Q1 2019