Artificial Intelligence is an Efficient Banker
The innovative use of artificial intelligence in the financial industry is no passing fad
The innovative use of artificial intelligence in the financial industry is no passing fad. It is a must and a trend that seems to have no alternatives.
Algorithms improve financial management and product selection for customers and automate the work of financial institutions.
The most useful innovations that have emerged in today’s financial industry would not be possible without some basic capabilities of artificial intelligence.
The main ones are to process large volumes of data, perform predictive operations and conduct real-time analyses of information sets. These alter not only the way banks and insurance companies operate but also the way customers behave.
According to Bain & Company, the savings made possible by the deployment of AI will run up to $1.1 trillion by 2030, amounting to a 22 percent reduction in operating expenses. These figures are consistent with the assessments of Accenture.
What follows is an overview of how smart technologies are changing the face of finance and an attempt to predict such changes going forward. What can we expect AI to do for the financial and insurance industries?
Uniform tools to feed data to AI
Apart from money, banks’ most valuable asset is knowing their customer. For years, customers have been researched in traditional ways and targeted with standard marketing.
This approach has had one fundamental flaw. Customer data was gathered and processed using mutually incompatible techniques and tools.
Marketing, customer service and sales each relied on a different data collection technique. The digital revolution made it possible to consolidate and harmonize these disparate areas.
Today, customer ratings, research on customer preferences and potential, product development and sales all take place in a shared digital space using mutually integrated and mutually compatible tools.
Data, the “lifeblood” of AI, may be fed from different sources but its underlying digital nature is a constant.
Real-time instead of history
Importantly, the majority of these processes take place in real-time. For a long time, the most essential variables for building solid customer relationships were age, income, occupation, marital status, and history of relationships with relevant financial institutions. Banks still continue to use all of them.
But they form only a part of the analytical puzzle.
There are a number of new factors these days, which include observing the current activities of customers (online activities, of course).
Companies learn what customers want and who they are by analyzing their behaviors on bank websites, in hotline conversations and in email and telephone interactions.
Adding further to the significance of this information is the ability to process it in ways that are virtually unlimited, and, most importantly the fact it can be processed in real-time.
A voice assistant inquires about a loan
One of the most dramatic changes to be expected in the coming years will affect customer service. The sector will see a rise in the use of voice bots — the kind we are already familiar with from our personal lives.
Some go as far as to call this an upcoming “Alexization” of our lives.
The term is a clear reference to the growing popularity of devices that support voice assistants, such as Alexa and Siri. The RBC Capital investment fund predicts that close to 130 million devices directly connected to Alexa will operate globally by 2020.
There is no reason why software based on the concepts utilized by such programs shouldn’t serve bank customers. All the more so given that bots trained by experts are increasingly better at imitating human interactions.
Bank of America has deployed the chatbot Eric to advise bank customers with voice and text messages.
The bot works 24 hours a day supporting all regular transactions that customers normally perform. This saves the bank bundles by replacing over a dozen if not dozens of specialists doing shift work 24/7.
In Silicon Valley, they say that unlike machine algorithms and the chatbots they support, humans are not scalable.
When a bot sounds like a human
Since self-learning machines improve over time, assistant devices will become increasingly more competent. A bot capable of carrying a conversation will no longer limit itself to answering questions about the weather or traffic. It may just as well advise you on loan interests and the benefits of opening a deposit.
It can tell you how much money you need to repay and remind you about an upcoming payment.
Today, devices of this sort do well in first contact with customers, sorting hotline callers, and putting customers through to appropriate departments. In the future, the duties of assistants will become more complex.
Obviously, for this to happen, the bots’ communication skills will require some honing. Skills such as using compound sentences, tuning into customers’ intonation, and sensing their underlying problems are still in development.
But the pace at which bots are learning is ever faster.
Money with a fingerprint
As a sector that collects and processes enormous data sets, banking faces a serious challenge. What makes it all the more serious is the fact that today’s users are highly sensitive to security threats.
Their anxieties are compounded by regular media reports on leaks of sensitive data kept by companies and portals. Banking and insurance IT experts have their hands full searching ceaselessly for ways to restore the recently undermined confidence of the average consumer.
Against this background, it is interesting to note the gradual changes in the way accounts are accessed online. In a nutshell, this concerns logging and authentication procedures.
The traditional approach relies on nothing other than the usual logins based on a string of characters. As hacking techniques improve, this becomes a critical vulnerability of information systems.
Bots designed specially to steal such data intercept security codes and passwords.
This calls for alternative solutions. By all indications, authentication may be revolutionized by the use of fingerprints and, coming up soon, face recognition.
This unique, personal identifier appears to offer the best and also the easiest-to-use protection against system intrusions available today.
Just as voice-enabled devices can made traditional search engines and manual data entry interfaces obsolete, character-string-based authentication will be rendered outdated by systems relying on the touch of the human hand.
Send us a selfie and we will give you a loan
And what about authentication based on a facial photograph? Is it even possible? It is beginning to be. The individual features preserved in a photograph represent unique content that cannot be forged.
This uniqueness can be uncoded by thorough analysis using software that identifies even the smallest nuances of an image.
The use of a scanner to enter a photo or the use of a laptop camera to scan in a face may well become the next authentication method. AI’s ability to recognize faces, which is increasingly valued, not least by the police, can be used in all kinds of contexts.
One can imagine loans being granted on the basis of signals “written” on a photo, which we’ll upload into a designated space.
Algorithms will be able to examine an image to provide a bank with information about our health and overall life situation, allowing the bank to assess our value.
Investigations and real-time surveillance
In view of these security considerations, it is also worth mentioning the use of AI tools to monitor the security of banking systems. Intelligent AI-based software is designed to track the smallest anomalies indicative of a hacker attack in real-time.
Every day, hundreds of thousands of hacks and theft attempts targeted at valuable data or financial assets occur world-wide.
Old type software would be unable to detect such attacks due to their sheer number. It takes AI tools to provide adequate protection and a sense of security.
And it is not just about such attacks. Banks struggle with the constant natural challenge of having to process and approve ambiguous high-risk transactions in cases that allow for multiple interpretations.
The difficulties arise in a wide range of fields from lending to loan repayments, to interest rate calculations, to a host of accounting operations.
People using traditional devices need hours to analyze cases and reach decisions to either approve or reject operations. Systems that rely on machine learning can authorize such transactions within mere seconds. The benefits to system efficiency and security are plain to see.
AI goes into insurance
These solutions are currently in use or will be used also in the insurance business. Here, too, artificial intelligence will be confronted with substantial amounts of data on customer behaviors and needs.
This industry’s preoccupation with risk is even greater than that of banking. It employs algorithms to assess threats to customers’ life or health and examine their property holdings (real estate).
It relies on the above-mentioned photo analysis technique. The sale of life insurance policies will require extensive analysis. From this viewpoint, new ways of gathering data attract much interest.
One possible source of massive amounts of information is the user’s car. Data on vehicle mileage, the driver’s accident proneness, and even driving style can be invaluable for companies developing personalized insurance products.
It may also be vital to use advanced algorithms to assess the likelihood of customers developing health conditions by living specific lifestyles.
We are yet to see whether future insurers will want to analyze bottom coded values, but the possibility cannot be ruled out.
Services tailored to customers and automated buying
AI will soon be able to perform the crucial task of reliably identifying people. This very ability will allow companies to handle every customer as a whole separate case rather than lumping like persons into larger sets.
At a more general level, this innovation will enable providers to tailor their products and services to the needs of individual customers.
There is also another solution that this approach will support. In the near future, customers will use interfaces that themselves analyze the data they enter. This stepin automating banking services as a logical consequence of deploying smart algorithms.
Shopping for Mr. Smith and traders
Continuing along this train of thought, it is worth noting that automation will increasingly support product purchases. While today’s sales personnel continues to be an indispensable part of customer relationships, future customers will make do with mere applications.
These may, for example, conduct serial sales of financial and insurance products and relieve customers of time-consuming decisions. Note that serial buying will be a life-saver for business investors who handle large volumes of products and information daily.
Professional traders will be able to count on AI to relieve them of many duties, which today are associated with both shopping and extensive analyses.
The power of algorithms and the mystery of the black box
Artificial intelligence can count on a very secure future in banking and insurance. Algorithms are poised to simplify many operations for the convenience and time savings of customers.
The sense of security that automation will give customers will become another revenue driver. As a side effect, one can undoubtedly expect impacts on employment in the financial sector.
One case in point is the bank JP Morgan, which has launched a platform to extract data from loan applications. It would take the bank’s employees 360,000 hours to go through 12,000 such documents.
Machine-learning software completes the job in, wait for it, a few hours. The benefits are evident. It is interesting to consider the event in terms of its impacts on employment policies.
Another vital question concerns explaining the inner workings of such algorithmic operations at a deeper level for both the sense of security of the customer and to show banks they are in a position to know what their system is doing.
This brings us to the recently topical issue referred to as the “black box” problem. It concerns everything that happens inside AI-run devices and that often defies human understanding.
Similarly to a lense, the revolution sweeping through financial markets brings into focus all the key issues associated with the presence of AI in our personal and business lives.
The only possible answer to the question regarding AI’s potential and its impact on the convenience of customers (financial product consumers) is: both the potential and the impacts are huge and unlike anything we have seen before.
The financial sector is one of the largest beneficiaries of algorithms’ rise to dominance in many industries. Such industries are nevertheless acutely aware of the scores of unanswered questions on the protection of funds, system security, customer data processing, and regulation.
One needs AI to be able to use money to make more money. But money also requires rules and regulations so that it doesn’t evaporate rapidly amidst technological turmoil.
Bain & Company, Karen Harris, Austin Kimson, Andrew Schwedel, Labor 2030: the collision of demographics, automation, and inequality. The business environment of the 2020s will be more volatile and economic swings more extreme, Link, 2019.
CNBC, Arjun Kharpal, Amazon’s voice assistant Alexa could be a $10 billion ‘mega-hit’ by 2020: Research, link, 2019.
Future Digital Fiance, WRC Insights, One Million People Are Now Using Erica — BofA’s AI-Powered Chatbot, Link, 2019.
FORBES, Martin Giles, JPMorgan’s CIO Has Championed A Data Platform That Turbocharges AI, link, 2019.
This article was originally published by Norbert Biedrzycki on medium.
I have been watching technologies change for years. I believe that the next wave of development is going to be driven by such technologies and trends as Virtual and Augmented Reality, Internet of Things, Robotics and Automation ana AI.