• AIP Team

The State of AI in Finance

Updated: Aug 13

Explore use cases of AI in finance, from robotic process automation to more accurate risk forecasting.

For many years, the financial industry has been a pillar of traditionalism; it’s an industry that’s stood on the shoulders of giants and all their financial wisdom to bestow. But because of this, the financial industry is also resistant to change and innovation. Old school financial service providers like banks and even venture capitalist firms have maintained the status quo rather than challenge it.


In the past few years though, newer finance companies started leveraging AI and other tech to modernize services like payments, invoicing, and more. This new wave of innovation is spurring the finance industry as a whole toward change, creating an exciting future of AI in finance that’s driven by quickly evolving consumer needs and digital transformation efforts.


While AI is still a relatively new fixture in finance (and the business world in general), the swift pace of change is moving more companies to embrace this technology than ever before.


The impact of AI in finance


From automating away finance teams’ least favorite tasks to creating a more even playing field for consumers, the current state of AI in finance is exciting to say the least. Here’s more insight into just a few ways that artificial intelligence is bringing the financial sector into the 21st century and beyond:


Risk assessment


One of AI’s key functions is being able to recognize and analyze patterns in data quickly and effectively. That’s a huge boon for financial service companies that issue things like credit cards, loans, and financial advising. Instead of simply deciding who gets to reap the benefits of their services based on often faulty predictors of risk like credit score, AI-trained machine learning algorithms can sift through data like people’s current number of active loans, number of existing credit cards, and past loan repayment abilities to create a more accurate picture of the risk each potential client brings to the company.


Analyzing and accurately predicting risk is an area that has long been a challenge for human workers in the financial space, not just due to the amount of time it can take, but because of the dozens of variables that can all shape potential risks. Financial employees at all levels go through extensive risk assessment training in order to minimize risk to the company, and even then, small details slip through the cracks. Machine learning can take a variety of variables into account without wasting teams’ precious time.

Credit scoring


Traditional credit scoring methods often employ very black-and-white processes that can fail to distinguish when a person may truly be creditworthy. Thanks to new advances in machine learning algorithms, modern financial service providers can issue credit scores based on a variety of alternative data points—like data from a person’s smartphone—to assist in providing a nuanced credit score.


Being able to distinguish between truly high-risk people and those who are dependable, but may not have an extensive credit history, is key to creating more inclusive and equitable financial services for people with a variety of life experiences and financial needs.


Fraud detection


Perhaps one of the most important applications of AI in finance today lies in fraud and cybersecurity threat prevention. By 2023, online payment fraud losses are projected to reach 48 billion per year. Today’s consumers are not only looking for convenience and ease, but top-notch security measures in their digital banking platforms. Artificial intelligence can help flag irregularities and other suspicious patterns that may go unrecognized by human workers. And as this tech continues to crunch data, it’s capacity to learn and recognize what is considered fraud—and what isn’t—grows as well.


Caption: While the future of AI in finance may not be exactly as futuristic as the media portrays it to be, it is a future to look forward to—one that’s more safe, dynamic, and accessible.


Stock trading


Being able to cast accurate predictions is a measure of success for many services within the financial industry, including managing investments and trading stocks. Fortunately, artificial intelligence is making it simpler than ever to analyze large amounts of data from the past and present to better give an idea of what the stock market might do in the future.


For individual traders, having AI on their side can be a huge benefit. Depending on a person’s appetite for risk, AI-enabled trading platforms can make decisions for users on when to hold or sell stock based on these analytics, or could send notifications to users when the market is expected to fall. Either way, having these tools at your disposal can help traders make more informed decisions, and help incentivize people that may not have an extensive knowledge of trading to start investing.


Robotic process automation


One of the simplest ways AI is transforming the financial sector is also one of the most important. Robotic process automation, or RPA utilizes AI to automate repetitive, often menial tasks like data entry and report generation that on their own are very time consuming and contribute little ROI to the business. By relegating these tasks to machines, companies reduce human error and allow human teams to spend more time on higher-value tasks and projects. One study from 2019 found that, on average, financial companies saved 25,000 hours each year by leveraging RPA. That’s 25,000 hours that can go into innovation and ideation, and extra funds to invest in emerging technologies.


What’s next for the financial sector?


While there is a lot of exciting innovation happening in the financial services sector, AI is still a relatively new concept for many companies. As more businesses look to embrace digital transformation initiatives, the prevalence of AI in finance will inevitably continue to grow and evolve. A few emerging applications of artificial intelligence in the industry that you may recognize today include:


Customized personal finance advice


New startups like Wallet are working to make the future of personal finance much easier by using data from users’ internet habits and spending patterns to offer customized advice and resources to track spending. And while privacy concerns are certainly at play here, the advantages may outweigh the disadvantages for people who struggle to keep their finances in order. To truly move the financial sector forward in this area, startups will have to commit to ensuring total data privacy and cybersecurity for each user on their platform.


100% mobile banking


While many people were beginning to realize the benefits of mobile banking before the pandemic, Covid-19 underscored the need for quick, convenient financial services that people could access from their homes. Nowadays, 78% of young people will avoid going to a physical bank branch if they can avoid it, meaning that traditional financial institutions are digitizing fast to keep up with new, digital-first FinTech startups.


Ultimately, the utilization of AI in finance is making it easier for companies within the industry to meet the needs and wants of today’s consumers. In a landscape where financial services need to be smart, convenient, digital, and secure, AI solutions are one of the best tools for the job. And as the sector moves forward, artificial intelligence will continue to play a role in helping people save, invest, and spend their money safely and easily.


But there are bigger strategies at play here, and finance isn’t the only industry being transformed by AI year-over-year. If your business is seeking new solutions to forecasted digital problems, AI may be the answer. Reach out to us to explore how AI can solve your business’s needs.


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