14 Jun Artificial Intelligence in Financial Services: Applications and Benefits
The growing adoption of Artificial Intelligence in the finance sector is opening new possibilities in customer expectations and the way they invest their money. Forbes reports that nearly 70% of banks and financial institutions (FIs) are already utilizing artificial intelligence and machine learning to forecast their cash flow occurrences and detect financial fraud.
Additionally, McKinsey reports that Artificial Intelligence or AI can deliver up to $1 trillion of business value each year. As the volume of financial and transactional data keeps growing, Artificial Intelligence is reshaping the finance sector by identifying powerful data patterns more efficiently than human resources.
In this article, we will look at some of the applications and benefits of AI in the financial sector.
AI applications in Finance
Here are some AI applications in finance that are driving the transformation:
After adopting AI and Big Data Analytics, asset managers have grown their revenues 1.5x faster than other financial services. According to data firm Barclay Hedge, 56% of their poll respondents were using AI tools in their investment process, while 33% were using machine learning or ML in risk management.
Traditional asset risk models assume financial markets to operate through linear relationships. On the other hand, AI models are being used to challenge “traditional” risk factor aggression. Financial companies use AI technology to model complicated risks and perform stress testing beyond the usual business scenarios.
2.Financial advisory services
A PwC report suggests the growing prevalence of robotic advisors in the financial sector. In a tight market scenario, financial institutions are under constant pressure to reduce their commissions on investments. AI-based machines can replace human financial advisors and save commission-related costs.
Additionally, bionic advisory services are combining the strengths of machine calculation and human insights to provide more efficient options. Thus, AI in financial markets is a key component in the financial decision-making process.
Another application of AI in the finance domain is in the area of personal (or consumer) finance. As more consumers seek financial security, AI is providing accurate insights into the right wealth management avenues. Similarly, AI-powered wallets are helping individuals make the right investment-related decision. This is driven by the accumulation of data from the individual’s digital footprint and spending history.
AI-based chatbots are also providing 24/7 financial advice to individual investors on where to invest their money and achieve their financial goals.
AI and ML technologies are also being deployed by financial companies to determine future patterns in financial markets. The success of digital or online trading is based on the ability to predict the immediate future of trading markets. AI-based models can effectively crunch large volumes of trading data within a short time.
AI models can also accurately detect anomalies (for example, the 2008 financial crisis) in past data and predict the market conditions that could trigger the next crisis. Similarly, based on a trader’s risk profile, AI models can recommend investment stocks to hold, buy, or sell.
Next, let us discuss some of the benefits of the use of AI in the finance sector.
Benefits of using AI in Finance
From automating tasks to detecting fraud, AI technology offers a host of business benefits in the banking and financial sector. Here are some of the potential benefits:
By the year 2023, banks can potentially save up to $447 billion by deploying AI-based applications. Besides, AI technology can save North American banks up to $70 billion by automating tasks by 2025. The use of AI solutions can reduce labor costs by improving the efficiency and productivity of employees. Further, through faster customer onboarding, AI can reduce both hiring and training costs incurred by banks and FIs.
2.Improved customer service
The deployment of AI-powered chatbots is delivering personalized customer service, which helps in meeting customer expectations. Effectively, chatbots are automating manual and repetitive tasks, so banking executives can now focus on rendering high-value services. Machine learning algorithms enable self-learning chatbots to improve based on their customer interaction.
The emergence of new-age Fintech companies has increased competition in the banking and financial sector. Traditional banks and FIs financial institutions are constantly trying to innovate and offer better financial products to their customers. The use of AI applications provides a distinct market advantage by harvesting the enormous amount of financial data accessible to organizations. Along with providing a personalized experience, AI is enabling banks to offer new services to their customers.
In the age of growing cyberattacks, banks and FIs need to comply with multiple data security and privacy regulations. Customers want their banks to keep their personal and financial information safe from hackers. Through automation, AI can effectively reduce human errors, which are responsible for over 95% of data breaches. Besides, AI technology can efficiently detect inconsistent data patterns, thus alerting banks to a possible attack.
The future of AI in the Finance sector looks promising as more banks and FIs leverage their data for valuable insights that can lead to improved decision-making.
Artificial Intelligence and Finance – Our perspective
At NuMantra Technologies, we believe that the use of AI in the financial sector can drive multiple use cases and applications. We enable help our banking customers to not only develop and leverage AI and machine learning models to detect useful patterns from their financial data but also automate these processes for better business outcomes.
As organizations look to develop and automate processes that support future business models, executing this using multiple applications poses a considerable challenge. NuMantra Technologies hyperautomation platform is built to ease this by offering all these capabilities on a single platform.
Before embarking on a digitization & automation roadmap, as a first step, we recommend organizations streamline their business processes with process mining. This is integral to developing high-quality data that can be leveraged to maximize the benefits of deep learning and AI. As future business models are developed and validated using ML/AI, financial services companies can quickly move to implement & automate the supporting processes using Robotic Process Automation (RPA) technology.
All his can be tracked and analyzed on the platform to ensure that business outcomes are in line with developed models.
With over 20 years of experience in the banking and financial space, we can help you gain a competitive edge using with the latest AI and machine learning technologies. Looking to maximize your returns from investing in AI and ML? Request for a product demo now.