The nature of the financial industry is rather conservative, so any new emerging technology is thoroughly considered and analyzed. Artificial intelligence has a green light, as it has been proven able to effectively eliminate manual tasks and improve the overall work efficiency. What is more, analysts believe that by 2030, the banking industry will save over $1 trillion thanks to AI.
Do you want to learn more about how beneficial can AI in financial services be? Are you curious which processes can be improved by artificial intelligence? Based on our experience, we have gathered some insights to share in this article.
What Do You Need to Know about AI?
Artificial intelligence is a self-descriptive term; it is a sphere of technology focused on creating machines able to perform human functions. AI software can learn from the data it receives, determine tendencies, and make decisions.
There are different popular AI technologies:
- machine learning – uses algorithms to spot hidden patterns without being directly programmed where to look;
- deep learning – involves more complex recognition techniques to analyze images, videos, and other data sources;
- natural language processing – aims at understanding human speech;
- internet of things (IoT) – focuses on interconnecting various devices in one functional network.
So, AI not just operates with different parameters and numbers, but is also capable of interpreting and reproducing the text as well as analyzing images. Currently, AI has many spheres of application, and financial companies can also benefit from automating manual tasks, improving efficiency, and reducing the role of human labor.
AI in Finance: 7 Use Cases
Despite its conservativeness, the finance industry is experimenting with numerous ways of applying artificial intelligence. If you are hesitating over where to start from, the best approach will be to learn from organizations that have successfully combined AI and banking.
Let’s have a closer look at seven most perspective spheres that can be improved by artificial intelligence.
This may be one of the most obvious answers to how to use AI in finance. Most financial institutions are already using some algorithms capable of automatically filling forms and gathering information, but artificial intelligence enables cognitive automation. You can use AI systems to review documents, recognize and extract important data, saving your employees from hours of monotonous work so they can focus on revenue-generating tasks.
For example, JPMorgan Chase has launched a tool called COiN, or a contract intelligence platform, that can analyze about 12,000 documents in a few seconds, saving 360,000 hours of work. What is more, the algorithm is more precise than human employees: it minimizes errors and improves the contact interpretation process.
Thanks to its impressive processing power, artificial intelligence is vital for risk management. Traditional analysis methods always compare data with a set of rules. Determined by a human, these algorithms are not agile enough to cover all real-time changes and tendencies, so they produce many false alerts that require additional reviewing.
Using machine learning, risk management algorithms can effectively monitor real-time activities in the market or any other environment and provide you with valuable forecasts. AI-driven tools can learn on past risk cases, analyze both structured and unstructured data, and point on the first signs of potential future issues.
Protecting information from fraudulent behavior is a challenging task for any financial organization. Therefore, fintech is intensively adopting AI-based technologies to cope with massive data analysis required for effective fraud detection.
For example, artificial intelligence can make automotive KYC checks more precise by analyzing client’s behavior for uncommon patterns. Using predictive analytics, AI-based algorithms can redefine the AML process to quickly analyze transaction data and identify suspicious activities. According to AYASDI’s case study, such automated systems not only save you from fraud-related expenses but also improve operational efficiency by up to 20%.
The payment giant PayPal had implemented AI to create advanced fraud protocols that allowed to lower fraud losses to 0.32% of revenue. Instead of standard linear models, they developed a deep-learning algorithm that can reveal patterns in real-time transactions, set rules for each profile, and deny access in case of violation.
When it comes to trades, investment companies usually rely on human-constructed models that estimate market changes using algorithms based on historical data. According to Preqin, there are over 1,360 hedge funds that use such software for the majority of their trades. Alas, because of static performance these models don’t fully represent actual tendencies and require human revision.
Artificial intelligence enables dynamic approach. AI can construct models capable of not only analyzing massive volumes of information but also continuously learning from it and improving their efficiency. They can use different sources, from financial data to tweets, to spot financial environment tendencies and predict their further changes.
There are several startups devoted to AI trading systems, such as Aidyia, the completely autonomous hedge fund capable of making its own trades without human intervention. Big financial institutions are also investing in such projects. For example, Goldman Sachs, one of the Wall Street’s top banks, invested $15 million in Kensho, AI-powered platform that had predicted Brexit-caused currency drop in the United Kingdom.
To cope with the most repetitive tasks, many businesses rely on AI-made predictions, employing robo-advisors: platforms for financial planning services. They can augment portfolio management by tracking a client’s account activity and using information from simple questionnaires to determine customer’s preferences and rebalance human decisions.
There are different approaches to robo-advisory. Smaller institutions usually involve some algorithmic components to automatically manage their investments, while larger firms can work with fully functional robo-advisors. For example, the company Invesco has bought an existing solution Jemstep for investment decisions. In any case, such services can save up to 70% investment costs making them vital for the wealth management industry.
Machine learning, one of the most used applications of artificial intelligence in finance, can outperform traditional approaches to the lending process. Credit managers can benefit from AI in banking by using AI-powered lending tools able to determine the character and capability of applicants in a short time. Without the human factor, such tools become more objective as they rely only on the collected data and not on their own biases.
Lending management applications can lover regulatory costs, decrease decision time, and provide more accurate assessments creating a more transparent landing process. Fico had employed AI to build risk models that group customers by their behavioral archetypes and benefited from 15% performance improvement.
Many people make the mistake of thinking that increasing automation will lead to less personal experience and reduce clients’ loyalty. It might be true for standard automated messages, but you can use AI-powered chatbots capable of more flexible communication. In addition to ready-made responses, such assistants can track numerous data sources to understand clients’ behavior and preferences so they can answer even less typical queries without consulting your experts.
Read also: How to Make a Chatbot
In fact, AI makes customer communication more personalized, enables 24/7 support, and eliminates the need in additional employees. Bank of America has already used this technology to launch a chatbot, Erica, to guide its customers via voice and text messages.
Some people are afraid that artificial intelligence can replace humans, causing massive layoffs all over the world. However, in the financial sphere, the human touch will remain critical as 81% wealth management clients look for face-to-face interactions. AI cannot totally replace human intelligence, but it can boost most financial processes with its massive processing power and accuracy, increasing the overall effectiveness.