The world of finance is well-known for seeking every possible edge to maximize profits; thus, using machine learning (ML) and artificial intelligence (AI) was a no-brainer.
Undoubtedly, artificial intelligence is evolving at a breakneck pace, and not even a single industry has been left untouched by its transformational impact. We can see it all around us!
While it’s true that AI is all around us, the stats also reflect this. According to FORBES:
70% of all Financial services firms are using Artificial Intelligence (AI) and machine learning (ML) to predict cash flow events, fine-tune credit scores, and detect fraudulent activities.
AI in the finance industry circumscribes everything from risk assessment to risk management, task automation, sales forecasting, and much more.
Let’s look at this comprehensive blog to understand better the areas of the financial realm where AI has the most influence and how the fintech industry can leverage it to make better business decisions.
Let’s get started!
According to the predictions of various Financial Experts, AI will have saved financial companies about $1 trillion by 2030. Moreover, more than 32% of banks are already using AI to decrease the response time for a customer, improve recommendation engines, and implement voice recognition and predictive analytics.
Financial institutions are now working more smartly than ever!
They use AI algorithms to keep crucial economic benefits and demand from tech-savvy customers as the forepart of their financial strategy.
In the world of AI, finance and banking will have become AI-first rather than using AI technology on their periphery. With precise implementation, they can improve their decision-making process and reduce risk, unlocking a trillion-dollar opportunity for the fintech industry.
However, the advantages and applications do not end here. Let’s look deeper at AI applications in the banking and finance sector.
AI Applications in Finance
Financial Risk Management
In finance sector, risk management is always a significant concern. Even after the perfect financial risk analysis, uncertainty in investment decisions still exists.
And to get away from this uncertainty, a solid understanding of risk in its various forms can greatly help.
But how can we get this solid understanding?
Given that AI offers incredible processing power and can handle huge amounts of structured and unstructured data, it can unquestionably handle risk management tasks much more efficiently than humans.
The financial crisis happened rapidly in the past, especially for credit-challenged customers. Customer intelligence relied on comparatively simple heuristics; customer value data was gathered via surveys and focus groups — which brings undesirable results.
Today, AI is doing wonders in banking and finance, providing tremendous amounts of data about consumers’ behavior and needs.
More sophisticated, Machine learning (ML) algorithms can identify the pinpoints, analyzes risk history, and detect potential risks before they occur. AI is a great fit and a significant upgrade for those financial areas that rely on statistical methods big-time, such as banks.
Is there any possibility that AI can decide whether an individual is eligible for a loan or not?
AI algorithms identify patterns of behavior related to past activities and backtrack them as risk predictors. For instance, a person applying for a loan can be immediately identified if he had a bad financial history in the past.
Fraud detection and Prevention
Fintech News in 2021 reported: countless Financial institutions, i.e., Banks, are deploying AI-based systems with a spent of a massive amount of more than $217 billion – to assess potential risks and prevent fraudulent activities.
And what happens when technology is highly developed and powerful? It becomes the primary target for fraud.
Like everywhere, various fraudulent activities happen in the finance sector cleanly, which is tricky to identify by human beings. Unauthorized transactions, phishing scams, and theft incidents are prevalent. In addition to that, massive financial data is stored online — introducing additional risks for security breaching.
But let’s not surrender to scammers yet; we have a secret weapon for identifying and preventing the scams: Artificial Intelligence.
After implementing machine learning systems, financial service providers can get hands-on with various techniques to encounter fraud, create rules that will automatically improve with the times, and provide the highest level of security.
The most promising thing about AI— after adopting it, 64% of financial institutions stated that AI could get ahead of fraud before it happens.
Unequal access to credit is a long persistent issue – knocking out the deserving ones from the mainstream credit ecosystem.
Now, where everything is digital, lending is also under the influence.
Previously, loan applications were processed by individuals — with an element of intentional or unintentional biasness.
Artificial Intelligence and Machine learning Algorithms assist financial firms in making credit decisions based on historical data. — more accurate, unbiased, and faster credit underwriting is made now.
AI-driven credit decisions can help improve the business with a reduced budget. Furthermore, the Sharp identification of risky customers made banks increase approval rates without increasing credit risk.
Profitability Ebbs and flows are the norms of every operational business — And to prevent potentially going under, sales forecasting is done in general and particular.
Financial forecasting typically analyzes past and current market trends to predict an outcome with a plan to increase business growth and save yourself from possible upcoming issues.
And one of the amazing things about AI-based sales forecasts is – the number of metrics it uses to draw a perfect forecast picture. You can feed AI with as many metrics and KPIs as you wish. For instance, you can take thousands of metrics, consider each for the prediction, and plan more accurately than ever.
Whereas you can be overwhelmed by large volumes of data, AI works better when it has more data to feed its algorithms. It can aggregate and process your financial data with far greater speed and accuracy— you only can imagine.
A 2020 JPMorgan study explains: “Over 60% of trades over $10 million were executed using algorithms. furthermore, the algorithmic trading market is expected to grow by $4 billion by 2024, bringing the total volume to $19 billion.
Well, Algorithmic trading is all about automating the trade using pre-defined algorithms.
With AI trading, you turn on the machine and let it update the algorithms automatically, handling all the transactions independently. It learns from multiple recent market conditions -– one of the most reliable metrics regarding decision-making.
Feeding AI predictions into algorithms can give you a more solid market overview so you can adjust your trading accordingly.
AI-enhanced algorithmic trading is particularly beneficial in meeting the demands of extensive clientele, including hedge funds, banks, propriety trading houses, etc.
Although banks have been slow to join the personalization era, they are now attempting to catch up with it after facing drastic changes in consumer expectations.
Customer segmentation practices based on demographic data, age, and other conventional metrics have become obsolete. While customers are ready for personalization in their banking routines, they expect more in return to make providing their data worthwhile. For this reason, many banks have now turned to a more personalization approach to meet today’s customer expectations.
AI can use transactional and bank data to understand customer behavior, increasing brand loyalty and driving revenue growth.
Furthermore, Chatbots can help satisfy customer queries regarding their account details and updates.
Personalized banking isn’t limited to chatbot assistance and saving money but also helps address financial exclusions and detect financial thefts.
Cyber attacks and Data Thefts
Cyber attacks occur commonly in every business space, but financial institutions (FIs) are the prime targets considering the huge amounts of cash and data they process daily.
According to UpCity Findings:
Cybercrime cost U.S. businesses more than $6.9 billion in 2021, and only 43% of businesses feel financially prepared to face a cyber-attack in 2022.
Without a solid cyber security system— financial organizations’ sensitive data could be at high risk. And to mitigate this continuous security risk, financial institutions have now adopted Artificial Intelligence.
An NLP-based cyber security system helps FIs carry out high-security checks and identify potential risks before affecting the whole system. Also, it already detects negative behavior patterns and isolates them instantly.
Machine learning ML algorithms are proficient at detecting fraudulent activities — they take real-time actions to prevent such activities.
However, as long as the financial world thrives, so will cyber attacks. But an AI-enhanced security approach can surely improve the FI’s security posture.
Financial Advisory Services
AI is reshaping the Financial advisory landscape — providing you with accurate financial advice based on your overall financial status, financial history, and financial habits.
A fantastic example of financial reshaping with AI is Robo-advisors.
Robo-advisors are AI assistants, an excellent option for entry-level investors, better at guiding users, personalizing user experiences, and, when necessary, connecting them with human experts. They provide financial advice or manage your investments with moderate to minimal human interference.
Robo advisors gather data by asking a set of pre-defined questions based on your income, assets, liabilities, and risk-taking ability. Moreover, it analyzes your financial transaction like bank and credit cards. That’s how they judge your economic behavior and make financial advice accordingly.
AI, however, is the ideal financial partner — frigid and unbiased, able to make huge calculations and find patterns in seconds.
While it’s true that AI and ML algorithms are dealing with numerous tasks, they are constantly learning from the volumes of data — bridging the gap by bringing the world closer to a completely automated financial system.
AI is inherently consistent, so it provides a clearer picture of what will work and what won’t, depending on past activities. And when it comes to financial decision-making, having this level of consistent understanding of the industry can help you make the right choice.