AI in financial services refers to the application of artificial intelligence technologies such as machine learning, natural language processing, and robotic process automation to automate and streamline financial processes, improve decision-making, and enhance customer experience in the financial industry.
What are Financial Services?
The finance sector offers financial services, which are economic services. They often offer qualified services involving lending, investing, and managing funds and assets.
AI in Finance
The use of artificial intelligence (AI) in finance is changing how individuals deal with money. Credit judgments, quantitative trading, and financial risk management are just a few of the operations that AI helps the financial sector expedite and optimize.
The market is expanding as well. Experts predicted that in 2021, the market for AI in finance would be worth $9.45 billion. They also anticipated for the market to increase by 16.5% in 2030.
It’s also critical to be aware of some of the major companies as the market grows. With this, let’s examine the areas of finance where artificial intelligence is gaining traction and spotlight the businesses that are setting the pace.
AI’s Role in Financial Institutions
The financial services industry (FSI) faces intense industry rules. Many characterized it as an industry that has intense competition.
These market forces have a big impact on how business owners and shareholders accept technology within their sector. So, financial institutions must always look for new ways to set themselves apart through technology. They can adopt no code AI strategies or hire AI skilled professionals for these tasks.
A strong technology that enables computers to predict future events by utilizing historical data sets to boost efficiency and enable new customer experiences, artificial intelligence, has a golden chance as a result of these dynamics.
The majority of financial services executives believe that artificial intelligence will soon play a crucial role in determining success as a result. NTT DATA Services’ 2021 poll found the following.
According to 83 percent [of financial services executives], access to special data sets is enabling the development of new approaches for AI. This allows them to distinguish offerings and win over clients. In the same study, AI, according to 81% of respondents, is essential to their plan to draw in and keep clients.
Financial institutions can optimize essential business processes while introducing cutting-edge goods and services. These advancements can enhance client experiences thanks to the AI in the financial services industry.
AI in Banking
Artificial intelligence (AI) in the banking sector enables organizations to automate mission-critical procedures. This includes processes like risk management and fraud prevention. It also opening up new possibilities like the usage of chatbots and intelligent recommender systems for retail banks.
Fighting Financial Crime
When it comes to investigating the funding of various illegal activities, banks are responsible for implementing a complicated system of rules and programs. These investigations touch on domestic and international activity.
For instance, the International Monetary Fund together with various nations, such as the US, developed anti-money laundering (AML) regulations. This was to mandate financial firms to have AML programs and report suspected actions.
Even though many of these measures have cost banks money, the rules have not prevented financial crime from happening.
Legacy hardware has acted as a barrier to success. This is because outdated systems lack the capacity to handle large datasets across numerous business divisions and battle threats. Real-time analysis is also becoming more and more necessary for AML safeguards in order to enhance online capabilities or permit speedier transactions.
Businesses are now using artificial intelligence to get around industry regulations and boost productivity through real-time analysis.
The clearest example of this found in PayPal. Paypal used Intel® technologies integrated with an Aerospike real-time data platform to increase the detection of fraudulent transactions. With this, Paypal was able to reduce the number of missed fraud transactions by 30 times, and to cut the cost of hardware in half.
The Connected Branch
Businesses can utilize artificial intelligence to create improved client experiences through new services and capabilities. They can do this to upgrade conventional operations.
For example, there are recent technological advancements in retail banking. They enable banks to comprehend clients’ wants and provide individualized financial services. These services cater to each person that avails of them.
Artificial intelligence-enabled machine vision systems inside the branch aid in bridging the gap between the real world and online channels. This can include such as kiosks located in the internet.
Machine vision-based sensors, for instance, can monitor consumer gaze, posture, and gestures. It can also gauge wait periods and notify bank staff when a customer needs help.
These artificial intelligence-powered tools examine online and branch-based behavioral data. The acquired intelligence can then customize and enhance the selection, location, and timing of marketing displays and campaigns.
AI in Capital Markets
Asset managers and hedge funds are utilizing artificial intelligence to increase productivity and introduce new features. This sets them apart from other financial organizations that operate in the capital markets.
Along with optimizing trading methods for a range of financial instruments, the technology is frequently utilized to help risk management procedures.
Liquidity and Risk Management in Trading
There is a set of international regulations known as the Fundamental Review of the Trade Book (FRTB). With AI, these rules can assist investment banks and other financial institutions by guiding them on how to work.
Financial institutions will have to estimate all risks related to their trading positions in securities, commodities, foreign currencies, and other investments starting in January 2023.
Due to the size of the project, FRTB compliance will be dependent on intricate financial simulations, modeling, and impact analyses, all of which demand significant investments in computing power and data storage capacity. AI can easily speed up the process of writing this study.
Software providers like Matlogica and Quantifi, for instance, improve performance significantly. This is because it uses a range of value adjustment (xVA) models based on deep learning and machine learning.
These AI-powered upgrades assist capital markets organizations in maintaining compliance. They do this all the while greatly enhancing the effectiveness of their risk models.
Algorithmic Trading
AI is also providing new capabilities in the capital markets, such as the real-time analysis required to support algorithmic trading. Trends determine how trades in financial markets will play out. These trends are patterns of previous market activity and transactions.
Recently, businesses have started deploying algorithmic trading, which uses machine learning, neural networks, and predictive analytics. This is to analyze market signals and react to them in microseconds.
Although algorithmic trading is not new, the near-real-time analysis required for traders to remain competitive is now accelerated by today’s AI capability.
The solutions from important Intel partners Aerospike and MemVerge that use 2nd Generation Intel® OptaneTM technology. This enables users to offer real-time storage and analysis that is necessary in the trading business serve as the best examples of this.
AI in Financial Services: Insurance and Payments
The insurance and payment industries are also utilizing artificial intelligence. It allows them to automate procedures, boost productivity, and provide new capabilities.
Underwriting and Claims Management
Companies in the insurance sector are implementing predictive models to use artificial intelligence. This can enable them to speed up the underwriting and claims management processes. Insurers can determine a candidate’s risk characteristics at a certain point throughout the customer onboarding process.
These more complex models use machine learning to examine a range of variables. This includes credit and health. Using this data, the models can provide a personalized premium for their insurance services.
Once a customer is onboard, insurance companies use AI to efficiently and accurately receive and process insurance claims. This makes it possible for clients to get insurance services swiftly and effectively.
Robotic process automation, a machine learning technique that permits hyper-automation of multiple operations, facilitates these processes.
Recommendation Engines
A recommendation engine is also used by credit card companies and payment processors. They use them to anticipate customer and prospect preferences.
When a prospect’s demographic profile and behavior either exhibit a distinct pattern of their own or resemble another group whose behaviors are known, the institutions then offer them individualized banking services. The machine learning-based recommendation engine examines a sizable amount of preference data. This is to determine which product and prospect are the greatest matches.
These engines are comparable to those used in streaming media services or e-commerce stores. It refers to engines that suggest additional products based on past purchases by an individual and related purchases made by other customers with a similar history.
Financial Services Technology
Flexible Platforms
As an example, Intel gives businesses flexibility with a range of technologies. These advancements’ designs aim to hasten the implementation of artificial intelligence.
The 3rd Generation Intel® Xeon® Scalable processor is the first x86 datacenter CPU with built-in AI acceleration. This makes it the perfect platform for a variety of AI applications.
Intel has all kinds of features that significantly accelerate AI workloads and encrypted data. Among these are the Intel® Deep Learning Boost (Intel® DL Boost), Intel® AVX-512, and Intel® Software Guard Extensions (Intel® SGX).
Real-time data analysis and computation for specific training workloads are possible because of other important Intel® products. These include the Intel Iris® Xe GPU and 2nd Generation Intel® OptaneTM persistent memory.
Software Optimizations
Although Intel is best known for its hardware, the corporation also makes considerable investments. They chose to invest in software tools, libraries, and partners to facilitate the widespread adoption of AI.
The 3rd Generation Intel® Xeon® Scalable processor, for example, optimizes all kinds of data science tools and libraries. It allows professionals to create and implement their own AI solutions.
The Intel® Distribution of OpenVINOTM toolkit, BigDL, TensorFlow, PyTorch, scikit-learn, and other optimizations enable developers to expand their AI environments. They can do this across nodes in a seamless manner, from the edge to the cloud.
Additionally, hundreds of commercial software companies optimize Intel® technology. They do this along with the biggest cloud service providers, and other open source organizations including the Linux Foundation and FinOS.
Customer Engagements
Last but not least, Intel has been collaborating with businesses in the financial services industry to address their most difficult difficulties for decades.
Financial services firms interested in implementing artificial intelligence into their organizations may count on Intel as a top leader in technology. The financial services sector must have the tools and resources it needs to compete worldwide.
Benefits of using AI in Financial Services
Experts expect the use of AI in finance to increase as it gives financial institutions a competitive edge in two key ways:
- Increase in efficiency: AI enables businesses to save costs and increase productivity, which increases profitability.
- Increased client satisfaction: New, highly-customized product options are now available to an increasing number of consumers thanks to AI.
Use Cases for AI in the Finance Industry
Personalized Banking Experience
Providing a context-based client experience is now a requirement, not an extra. In the fiercely competitive world of banking and finance, it is a requirement that all institutions must meet.
Using AI systems reduced customer service wait times. They also speeded up with the introduction of chatbots and virtual assistants. These are the byproducts of the AI revolution in the banking sector.
Clients may quickly analyze their bank account activities, plan monthly payments, and check the balance of their accounts.
Fraud Detection and Risk Management
AI-based systems assist customers in reducing risk and preserving money from fraudulent actions. It is because of their fraud detection capabilities.
By analyzing enormous volumes of data, AI solutions in the finance sector may do everything from recognizing unusual transactions to pinpointing questionable and potentially fraudulent activity. AI can swiftly gather data that assist safeguard businesses from losses and boost customer ROI.
Challenges and Limitations of AI in Financial Services
Data Concerns
There must be a method for quickly identifying abnormalities throughout the entire pipeline, identifying the issue, and fixing it. Many companies are based on this concept, and some even provide git-like version control for their own data.
Localization is a problem that data has as well. Multinational financial organizations frequently have to develop models that span the various markets they service. To properly adapt the client experience, the data must be consistent across diverse languages, cultures, and demographics.
Dimensionality reduction
Experts say that financial institutions sit on data troves because a single transaction can include hundreds of data pieces.
Many machine learning techniques tend to struggle as the number of attributes increases. The analysts must either perform some sort of feature selection or make an effort to reduce the dimensionality of the data.
FAQs
Why is AI the future of financial services?
Although the banking industry has always been heavily dependent on technology and data, new data-enabled AI technology has the potential to accelerate innovation more quickly than before.
AI may help raise differentiation, manage risk and regulatory requirements, increase efficiency, support a growth agenda, and improve customer experience. Many expect the banking sector to be significantly impacted by the increasing use of AI.
Banks are making considerable advances in implementation and acceptance. This comes despite the fact that they still face numerous organizational and operational hurdles.
Banks must stick with their current trajectory and continue to develop the technological underpinnings. Banks need to continue these procedures to advance into the future in order to fully profit from AI.
How is artificial intelligence used in finance?
Corporate finance benefits greatly from AI since it can more accurately identify and evaluate credit risks. Machine learning and other AI technologies can enhance loan underwriting and lower financial risk for businesses wanting to raise their value.
What is the application of AI in banks?
Over the past ten years, customer expectations in the banking industry have gradually risen. In the past, people preferred to simply walk into the bank and finish up all their jobs quickly.
People today prefer to complete all transactions online. For single employment, people are not prepared to commute to the banks. Banks can establish a communication channel for a better user experience by adding chatbots for banking. This is one way of implementing AI in the banking industry.
