Artificial intelligence in FinTech

FinTech : It is just starting

FinTech sector is producing businesses with scalable products and has seen rapid growth over the past few years. Senior executives at banks are responding to the challenge these companies have started by setting their own incubators up to capture this high-speed initiation.

Technology was once centralised, with companies being run on big databases and transaction engines. Nowadays, it is massively distributed. New businesses have sprung up to take advantage of the chances this shift brings, while leading banks still operate using the old technology. The term “peer to peer” captures some of the phenomenon, in that it is now potential for financial transactions to take place on a platform without needing a bank or indeed any entity as an intermediary.

The financial services marketplace is all about information exchange that is reliable, secure and efficient. In many cases the new alternatives can be cheaper and quicker than traditional models. A broad variety of potential models exist, which explains the increasing number of new fintech startups that have entered the market.

Needless to say, fintech is not new and technology has consistently brought gains to consumers. Back in the day, however, development costs were high, while the technologies of today are more broadly available, affordable and, most importantly, worldwide scalable.

The huge banks are setting up their own initiation arms to investigate opportunities presented not only mobile but also by by P2P and micro-payments cryptocurrencies like Bitcoin,, and distributed ledgers for example blockchain.

But as progressive as traditional financial institutions strive to be, they will remain hampered by their legacy systems and processes. I see the banking landscape continuing to change quickly as fintech businesses with talented management, viable products and clever advertising using new and traditional media take market share. Moving fast, nimbly and economically to capitalise on opportunities is the key.

Artificial intelligence in FinTech

Since its inception in the 1950s, artificial intelligence (AI) has found at least two major boom cycles and long winters of disillusionment. While artificial intelligence endured through the recent disullusionment cycle in the 1990s to today, its easing and corollary technologies have flourished, and we’re now entering into a fresh boom in applictions of the technology.

Financial services have been revolutioned by the computational arms race of the last twenty-plus years, as technologies like big data analytics, expert systems, neural networks, evolutionary algorithms, machine learning and more have enabled computers to crunch much more varied, diverse, and deep data sets than ever before.

While most of the businesses built around machines making decisions are’t true AI, they may be using data-intensive technologies that will help technologies and firms continue to get closer to executing AI in commercial applications.

Despite the hype of intelligent machines, the first uses of AI are’t replacing humans and human intelligence but augmenting them. Text-based conversational chat was adopted by many startups as a way to deliver a personal assistant-like expertise in many industries, and in fintech we’ve seen the case of businesses like Kasisto utilising AI to scale the impact of people using technology. Instead of being bounded in customer support uses by humans reacting to users through chat windows, AI and related technologies are being implemented to deliver a human-like chat encounter without the need for nearly as many human helpers.

By using smart agents that can examine and crunch data about individual behaviour and compare to broader datasets, small and big businesses could have the ability to deliver personalized financial services as a scope and scale never possible before. Consumer banking, advisory services, retail financial planning, investment advice and wealth management, all of these services can be delivered using a conversational user interface with artifical intelligence software behind. The combination of technologies can empower firms to supply services to customers where they were unable to supply human service profitably (i.e. lower net worth sections of personal financial, investment and retirement advisory), but can now function using codified knowledge and AI-powered software.

In addition to new segments, they are able to be more personal, supplying guidance at the transactional level (i.e. every individual transaction). This is the story behind smart wallets like Wallet.ai. Picture having an assistant with you to allow you to assess, price, and consider every single thing you spend money on, at a granular level that you could not be assisted by any human helper with. Is a roboadvisor that offers rule based advice using only a couple of predefined parameters AI? Likely not, but newer technologies as time goes by which are based around learning and viewing about your behaviors at the individual level, could give guidance and outcomes which might be personalized in a way never possible formerly.

AI can also power technologies that overlay humans to supply workers activities with an tracking and oversight mechanism, helping with compliance, security, and the observation of employee actions. Monitoring discrete, repetitive data entry tasks, computers could watch and learn as time passes to verify test and data entry for particular events, evaluate danger, and find fraud. Any segment of fintech that is regulated creates the chance for companies to install AI-powered employee and systems supervision.

AI technologies that allow computers to process information could augment underwriting and lending products and make decisions more easy and better than individuals alone. While it’s still to be determined how new data sets created by technologies like wearables and internet of things can be used for insurance and credit decisions, AI-based technologies make it more potential for businesses to use these new datasets in highly personal ways .

But AI is creating bigger opportunities to go beyond testing and fitting data to create trading systems and more “intelligent ” dealers, using robotraders to optimize and test predictions and trading rules. AI can help manage and augment rules and trading decisions, helping process the data and creating the algorithms managing trading rules.

Some investment firms have implemented trading algorithms based on sentiment and insights from social media and other public data sources for years, but technology companies like Dataminr are installing platforms for a larger set of businesses to use. Getting and utilizing large, heterogenous datasets is now potential for far more companies to use, so how will companies leverage and build on top of these datasets?

The future of AI in FinTech

While much of the investment in artificial intelligence has been into multi-purpose platforms which are figuring out their specific, high-value usecases, the chance in fintech is somewhat different. Fintech has a base of technological prowess in the technologies supporting AI and several immediate high value uses.

Initially, AI was used more in backend technology settings to power large scale decisioning in financial analysis , trading and lending, but now it is becoming a technology that expands how everybody interacts with financial services companies. A number of problems consumers are facing when using financial services are around the problems in getting to quality, personal service. And possibly it’s an artificially intelligent agent that helps deliver cheaper, private services that are better and faster.