FinTech agents and some traditional financial industry players have gotten stronger throughout the pandemic crisis. Many financial companies have been affected, but many more are quickly adapting to offer financial services adapted to the world’s new reality.
Some companies in the finance industry had already been
strengthening their business models with state-of-the-art and innovative HiTech
solutions even before the pandemic started. This process has now been
accelerated. In particular, Artificial Intelligence (AI) and Machine Learning (ML) are redefining how many things are done within the financial
industry.
As more financial activities are done through apps,
companies can obtain powerful insights through new data points. This, in turn,
allows new disruptive technologies to create many opportunities for users and
companies alike.
However, almost everyone thinks that AI and ML are only for
big companies with tech experts and large pools of capital. Nothing can be
farther away from the truth. FinTech companies, large and small, are using these
technologies, paired with powerful apps, for all sorts of purposes.
We’ve already discussed the differences between AI and ML and some of the uses of ML. This post discusses some of the most
important ways FinTech companies use AI and ML. Hopefully, you
can get some inspiration from them for your business.
AI in FinTech: Use Cases of Artificial Intelligence and
Machine Learning
Financial technology is far from replacing human
intelligence but can surely augment its powers. By using computer-based
tools that rely on Big Data analytics, financial firms can harness the power of
tools like an Artificial Neural Network or other disruptive tools to build
powerful products and decision-making solutions to innovate financial services. This is generating important changes both at an
organizational and human scale.
AI in FinTech has the potential to help companies achieve
their growth objectives, gain a competitive advantage, and make them more
relevant to their clients. It can also help them reduce
operational costs and make internal processes more efficient. Users can benefit
from this through better personal financial management.
These are just a few examples of the most important uses of
AI and ML algorithms in finance.
Improved Financial Decision Making
FinTech apps are developing new and interesting ways in
which users can process information. Thanks to the power of data science and
visualization tools, analyzing data through apps becomes easy, transforming it
into digestible insights. As a result, users can use complex
information to improve their financial decision-making.
Security & Fraud Detection
As digital transformation processes take over the world,
financial cybercrimes will also grow. The silver lining is that thanks to AI and ML, companies and users can now secure themselves and their
accounts.
Cryptocurrencies and blockchain are often associated with
financial cybersecurity. However, we will shortly associate AI
and ML with digital security and anti-money laundering solutions. Algorithms
can detect suspicious activity, and even better, they can notify
users. These technologies can continuously monitor unusual patterns, so there
is no need to be vigilant 24/7. Users can keep track of everything behind their backs, confident that their assets are safe.
There has also been a great impact on behalf of these
technologies regarding detecting other illegal activities like money
laundering. Thanks to AI and ML, governments and other institutions have the power to use an army of bits and bytes to trace corruption networks.
Asset Management
Investment funds have been using complex algorithms to develop robust forecasts and simulations. Thanks to this, the
asset and wealth management world has been able to restructure many of its
processes and offer new services like wealth management tools. FinTech firms
have noticed this and are implementing these solutions into apps so users can take advantage of them.
App users can now manage bank statements and make important
transactions directly from their devices. Most importantly, thanks to AI
and ML solutions, users can choose to reduce the number of
intermediaries. As a result, wealth management has removed unnecessary processes, helping reduce operational costs.
Customer Support
Bots are one of the most famous AI applications. Although
they have been around for some time, only recently have they started gaining traction thanks to ML algorithms. We are now seeing the rise of potent chatbots
that can interact with customers to produce an immediate response to several customer requests.
FinTech companies are using bots as a major channel to solve
customer issues. Robo advisors and automated customer support are some of the
most common ML solutions. Results have been impactful as chatbots allow
companies to reduce costs and increase customer satisfaction.
As physical distancing becomes the new normal, financial
institutions will increasingly opt for this technology to solve
customer issues, improving the Customer Experience. Brick and
mortar offices are expected to be around for a while but will most
likely be relegated to specific activities.
Insurance
One of the most innovative ways AI and ML are used is to reshape how insurance policies are evaluated. Because this industry
is heavily driven by financial tools, FinTech apps are used to determine
risk levels. Companies can calculate someone’s level of risk through their
activity.
This has been used with success by the auto industry. A
combination of IoT technologies and FinTech app development has allowed this industry to calculate a person’s risk level by assessing
their driving skills through a mobile app.
Smart contracts that use technologies like Blockchain and AI
are also being used to innovate within the insurance industry.
Loans
This is the most popular way FinTech companies benefit from HiTech. The world has seen a wave of money
lending apps thanks to the possibility of using someone’s financial habits and
credit exposure to calculate their credit scoring, making the underwriting
process more efficient without human intervention.
Loans through AI and ML can be done faster while
reducing inefficiencies. Additionally, they tend to be more accurate than the
traditional underwriting process, thanks to an improved client risk profile
approach. Some experts even argue that this might help customers by reducing
biases that can occur through human decision-making.
Although this last is true, the opposite, negative biases,
can also occur. Agents that use these mechanisms need to ensure everything works out in calculating credit scoring. Otherwise,
they risk segregating an important pool of users from their services.