Artificial Intelligence (AI) in the finance sector encompasses a wide variety of applications from fraud detection to chatbots to automated credit decisions. AI has provided the financial industry with the means to meet the demands of customers for smarter, more convenient, and safer ways to manage their financial assets. This has led to the financial sector being one of the industry leaders in AI adoption and exploring the expansive opportunities that exist to combine data and technology to create benefits for all stakeholders.
Let’s start by looking at how AI is used across different types of financial services.
Use of AI in Financial Services
Finance can be broadly divided into three categories:
- personal finance
- corporate finance
- public finance
The following is how AI is used in each domain:
1. AI in personal finance
Customers are eager for financial independence, security and quality service. AI is one of the key technologies that is empowering them to independently manage their monetary wellbeing and protect their hard-earned assets. With the ability to provide day in and day out financial advice through chatbots and 24/7 customer service line, AI grants customers confidence and assurance. Capital One is a pioneer in employing AI and in 2017 launched the virtual assistant Eno to provide financial advice and customer service through text messages and app chatbots any time of day.
Another valuable use-case for AI in personal finance is fraud detection. AI can dissect and single-out inconsistencies in banking transactions that indicate fraudulent activity which would easily be missed by human eyes.
2. AI in corporate finance
Artificial Intelligence is especially useful in corporate finance to predict and avoid potential credit hazards. For organisations looking to build up worth, AI can assist to diminish financial danger to the largest extent and decrease man-made accounting errors by spotting irregularity. AI is also aiding banks and credit lenders to make smarter credit decisions by accurately assessing histories of borrowers. This process helps banks make safer loaning decisions to guarantee revenue.
3. AI in public finance
AI has great potential to solve problems that financial agencies face. By strengthening controls and spotting anomalies in big data, it helps reduce accounting errors, prevent tax fraud and financial crime and even forestall large scale financial crisis. What’s more, by automating repetitive work, AI allows agencies to free up human power and focus them on the more important tasks -drawing insights from data to make more profound decisions.
One example of AI in public finance is the ability to prevent financial crimes, according to EY. Governments could use AI to monitor countless transactions that happen in each country to detect anomalies, in order to stop money laundering.
Advantages of AI in Finance
Here’s a quick summary for advantages of AI in all different domains of finance from the example given above.
24/7 customer service for a wide range of customer queries
- Automation of highly repetitive tasks that frees up staff to work on tasks requiring relationship management or critical thinking
- Reduce human error and increase efficiency and precision.
- Improve financial security through applications such as transaction monitoring and fraud detection
- Loan suggestions made by AI are fast and reliable.
What is the future of AI in finance?
More data, more automation
At its core, AI is the creation of intelligent systems able to perform tasks that traditionally have been performed by humans. By combining the abundance of data with the right technologies, finance has been one of the most successful industries in creating tools that automate both trivial and non-trivial tasks and providing tools that augment human workers. In the coming years, we will see the continued growth of data and interest for how AI can improve efficiency, productivity and fairness. The use of financial data is highly sensitive and safeguarded and ethical considerations such as fairness, equality and privacy must be at the core of new applications. Advances in synthetic data generation methods may provide the breakthrough needed to improve data exchange across institutions and increase opportunities to use AI in a commercial context.
The rise of NLP and voice recognition
Natural Language Processing (NLP) is a branch of AI undergoing rapid advancement and is being adopted in more sectors and contexts to provide more effective, satisfying and natural customer interactions with products and services. For AI in finance, this is likely to mean a shift towards better customer service facilitated by chatbots or more meaningful financial advice from personal virtual AI assistant.
Ethics, fairness and equality
AI offers significant growth prospects for the financial sector. But this potential is only achievable and sustainable if the organisations responsible for the development and adoption of these tools to meet certain standards regarding fairness, equality and non-discrimination. This requires careful consideration of many different areas such as incorrect, corrupt or non-representative data sets that result in algorithmic bias that systematically disadvantages certain groups of individuals. Mainstream acceptance of AI in our lives is contingent on demonstrating it creates positive outcomes for the many, not just the few. In the future, industries can expect to be asked for certain high-risk applications not only what decisions AI is responsible for making but to explain how and why certain decisions are made and to have transparent and auditable systems in the event of a dispute.
Artificial Intelligence has wide-spread usage in the financial sector at each individual finance category, and it will no doubt become one of a game-changers in the future of finance. People and financial organisations will and need to embrace this inevitable trend. With technological advances and appropriate regulations, AI will sure take financial services and activities to the next level.