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AI in Finance & Banking: 11 Ways It’s Changing the Industry

10 minute read | November 1, 2021
Sakshi Gupta

Written by:
Sakshi Gupta

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In 1950, when Alan Turing questioned whether machines could think, no one knew how much capability artificial intelligence would have more than 70 years later. This question alone gave rise to complex concepts like machine learning (ML), robotics, deep learning, expert systems, neural networks, and all of what we now know as artificial intelligence (AI).

‌Over the years, AI has found its way into every single industry, disrupting the norm and status quo. The banking and finance industry hasn’t been left out and below are some of the ways AI in finance is redesigning the industry.

‌How Is AI Impacting Finance?

How Is AI Impacting Finance

‌As AI establishes its presence in the world of finance, it brings with it numerous impacts—both beneficial and detrimental.

According to Intone Networks, AI has positively transformed the banking and financial services industry by:

  • Minimizing operational costs
  • Improving customer support
  • Improving risk management
  • Automating routine processes
  • Increasing service speed
  • Improving data processing accuracy and speed

AI is set to save the banking industry approximately $1 trillion by 2030 and $447 billion by 2023, as discussed by Business Insider.

Despite all the good that AI brings to the world of finance, it also has some negative impacts. According to Forbes, AI exposes the industry to risks such as cyberattacks, credit risk miscalculations, and the much dreaded wiping out of human capital and employment—which will be demystified later in this article. It is, however, undeniable that the beneficial impacts of AI outweigh the risks.

‌How Is AI Being Used in Finance?

How Is AI Being Used in Finance

Artificial intelligence is changing the way we deal with money in finance. From credit decisions to quantitative trading and fraud detection and prevention, there are plenty of use cases for AI in assisting the financial and banking industry. Let’s explore 11 different ways AI is being employed in finance and banking.

1. ‌Investment Management‌

Investment Management‌

As AI deepens its roots in the financial industry, it is becoming more and more crucial for investors and asset managers to find a way to integrate it into their investment processes. With AI, investment managers can analyze technical and fundamental datasets, build predictive models, and generate investment ideas faster and in real-time. AI also makes investment a passive process that requires minimal supervision, which reduces costs while bringing in money at the same time.

AI can also be used in:

  • Portfolio management
  • Risk management
  • Compliance oversight
  • Automated analytics
  • Automation of office tasks

As revealed by Deloitte, Man Group’s hedge fund—which manages more than $12 billion using AI—has quintupled the fund since 2014.

The potential of AI in investment management is yet to be fully tapped. The Alan Turing Institute reported that around 9% of hedge funds utilize AI and machine learning for investment and asset management. This number is, however, expected to increase over the years.

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2. Risk Assessment

An increase in the amount of financial data being processed by organizations calls for an increase in the accuracy in which this data is being processed. This, however, is near impossible for human hands and brains. This creates leeway for fraud risk, which is ravaging most financial services companies these days. A recent PwC report revealed that 47% of companies investigated were victims of fraud, with an average of six incidents reported per company.

To detect even the slightest risk, a company needs to analyze every single transaction in large data sets. This is where AI comes in. With the help of AI auditing software, financial institutions can now mine through mountains of data in a short time and, with precision, flag out anomalies that aid in risk assessment and mitigation.

‌Since AI eases auditing work, financial institutions no longer have to conduct quarterly or yearly audits. They can have the AI running monthly analysis instead. Continuous auditing enables companies to pinpoint and mitigate risks as soon as they appear.

3. Credit Evaluation

Credit Evaluation

‌To determine whether someone is eligible for credit, financial institutions conduct a credit evaluation process. This process involves:

  • Obtaining relevant information about the applicant
  • Analyzing the collected data to determine the applicant’s creditworthiness
  • Deciding whether to extend credit to the applicant
  • Deciding the amount credited

This procedure is constrained by both time and cost. Businesses run the risk of losing a potential customer if the credit request takes too long. They also run the risk of losing their money if the evaluation process is done incorrectly.

With AI credit evaluation, however, such risks are rendered powerless. Credit evaluation is based on a lot of data, including credit card history, payment history, the amount owed, and length of credit history.

As much as AI helps in combing through such a bulk of data quickly, it possesses the ability to enhance the evaluation process significantly. Credit evaluation based on history is a barrier to new customers even if they are creditworthy.

AI credit assessment can evaluate credit scores based on both historic and forecast data. This way, new customers, students, and startup founders can overcome the historical-based credit barrier. AI credit evaluation stands to benefit financial institutions by bringing in more customers while reducing risks. Customers, on the other hand, get better and unbiased access to credit services.

4. Securities Trading

AI trading solutions allow computers to:

  • Think independently
  • Analyze historical market data
  • Develop strategies based on that data
  • Make trading decisions
  • Manage risk

Some studies reveal that AI-led hedge funds have the edge over others. These funds have generated an average return of 34% in three years compared with 12% for the global hedge fund industry.

5. Risk Management

Financial institutions always face risks, including:

  • Identity theft
  • Credit risk
  • Fraud risk
  • Underwriting risk

AI mitigates such risks using advanced analytics and predictive analytics to spot specific patterns and reduce risk when these patterns are violated.

A good example would be in the case of financial identity theft. The AI would notice anomalies in purchase behavior and block the card before further damage ensues. AI can also predict the probability of the consumer defaulting and prevent the credit from being extended, thus saving the bank from a bad loan.

Infopulse pinpointed the three areas of risk management that would most benefit from AI:

  • Early warning systems—for credit risk management
  • Stress testing—for market and economic risk management
  • Data quality—for fraud risk management

6. Fraud Detection

Fraud Detection

Fraud has long been a menace to the financial and banking industry. According to Crowe, fraud chews up $5 trillion from the global economy each year, and that number continues to rise. Traditional fraud prevention methods are giving way to machine learning, which showcases greater efficiency at the job.

Modern fraud detection systems can continuously learn from previous fraud tendencies and spot them in future transactions. Since they also ease the data analysis work, fraud analysts are more efficient and can focus on what matters.

Highmark Inc. has saved over $850 million in fraud prevention in the last five years, thanks to AI fraud detection and prevention. Now imagine how much the global economy would save with AI.

7. Personalized Banking ‌

In the current business world, customer satisfaction is key to building long-term relationships and customer loyalty. According to a study by Accenture, of 47,000 banking and insurance consumers surveyed, over 80% would be willing to share their personal data in exchange for personalized services.

To match these demands, banks and other financial institutions should utilize the power of AI for their competitive advantage. Tools such as chatbots and predictive personalization AI help in:

  • Simplifying interactions with the consumer
  • Personalized mobile banking
  • Advising the consumer on their spending and savings
  • Protecting against fraud
  • Sending personalized promotional messages

8. Debt Management

Debt collection and management continue to be an uphill task for many businesses. CNBC revealed that the average debt of an American citizen stands at $90,450, with the amount increasing annually.

Thanks to AI, though, debt collection doesn’t have to be a complicated, unproductive, and old-fashioned process. Companies can now use behavioral science, data analytics, and machine learning to automate, ease and make this process effective while maintaining good consumer relations.

As outlined by Receeve, AI will reshape data collection by:

  • Utilizing statistics and data to improve repayment rates
  • Using behavioral science to develop debt collection strategies for individual customers
  • Automating the payment process
  • Using AI for A/B testing

9. Customer Service

Customer Service

Maintaining good customer relations is pivotal to the success of every business. To ensure optimal customer satisfaction, vendors ought to employ AI customer service tools.

Interestingly, customer service is the second most common use of AI. The field was also predicted to receive the most AI investments in 2020, amounting to $4.5 billion. The use of AI and data science in banking customer service is expected to automate 90% of customer interactions through chatbots by 2022, according to the 2019 Chatbot Report.

Some of the ways AI is being used in customer service is through tools like:

  • Chatbots
  • Email bots
  • Callbots
  • Face and voice recognition

10. Compliance Oversight

Compliance plays a significant role in the financial sector by ensuring businesses follow both internal and external rules. This ensures market fairness, efficiency, and transparency and protects financial institutions from the risks involved in violating these rules.

In the past, compliance officers were tasked with digging through various communication channels, searching for anything unethical and/or unlawful. This work was costly, time-consuming, and prone to many errors and risks that arose due to these errors.

However, with AI, compliance oversight has been simplified. AI enables compliance teams to sift through multiple sets of data in the shortest time possible with superb accuracy.

The beauty of AI is that, through ML, compliance isn’t only based on a specific set of rules but also on anything new that is outside the norm. In case of a new anomaly, AI takes in the new rarity and improves the whole compliance process. This makes compliance a self-sustaining system in an organization.

A recent survey conducted by NICE Actimize revealed that 89% of the participating companies confirmed that they were on the path to using AI for compliance purposes. Although reports have been made of invasions of private data, discrimination, and bias—which led to the development of the European Union’s AI Act — it is time to embrace AI as the new compliance officer.

11. Market Research

Market Research

Research is a crucial part of the financial industry—it is the engine that drives decision-making. Before a hedge fund invests in a particular stock, market research must be conducted. Before a bank or insurance company launches a new product or service, market research must be undertaken.

Market research involves loads of data that is analyzed and translated into insights, forecasts, and trends. Forbes reports that traditional market research is not only costly and slow but is also a closed resource that blocks the advancement of knowledge. The cost of conducting traditional market research is, on average, approximately $30,000.

With the advent of AI in market research, financial institutions can now sift through billions of datasets and develop strategies, products, or services at a significantly lower cost and in less time than traditional market research.

For instance, traders can test the viability and profitability of their strategies based on historical market data, giving them the ability to forecast their trading performance before committing real money. All within very short timeframes—and all thanks to AI.

Which Industries Are Benefiting the Most When it Comes to AI in Finance?

Which Industries Are Benefiting the Most When it Comes to AI in Finance

Within the financial industry, some sectors are profiting more from AI in finance in comparison to others, and these are:

Banks

There’s no denying that banks have benefited the most from AI in the financial sector. Through AI, banks have been able to:

  • Reduce operational costs through Robotic Process Automation
  • Improve customer relationship management
  • Enhance fraud detection and regulatory compliance
  • Improve credit evaluation processes
  • Automate investment processes
  • Improve debt management

Retail, business, and investment banks make up the lion’s share of the finance sector. This is why they are also the biggest beneficiaries of AI in finance.

Funds and Investments

The other significant beneficiaries of AI in finance are hedge funds and investment firms. These firms have benefited from AI in the following ways:

  • Automated trading systems
  • Instant, data-backed high-frequency trading
  • Enhanced data analysis
  • Improved risk management
  • Enhanced ability to generate alpha

Insurance

Last but not least, insurance companies have benefited from AI in the following manner:

  • Data-based individual pricing for consumers
  • Automation of claim management
  • Automation of payouts
  • Enhanced fraud detection

Pros and Cons of AI in Finance

AI brings with it its fair share of benefits and drawbacks, especially for the financial world.

Some of the pros of AI in finance are:

  • Faster and more accurate analysis of loads of data
  • Enhanced risk assessment and management
  • Improved fraud detection and prevention
  • Elimination of human error
  • Improved data quality
  • Better decision making

Some of the cons of AI in finance are:

  • High implementation and maintenance costs
  • Inability to make judgment calls
  • Kills human creativity

Future of AI in Finance

AI in finance is still in its developmental stages, and it continues to proliferate with the help of data scientists. Despite the disruptive innovations that have stemmed from it, it’s undeniable that more incremental and architectural innovations will crop up in financial AI in the coming years.

There isn’t a moment in history where humans have stopped innovating. Based on the continuous improvement of AI, it is only wise to agree that AI will further deepen its roots within the world of finance and continue to find more uses.

Some AI improvements that the finance sector might see soon include:

  • Increased data sharing between financial institutions
  • Improvement in Natural Language Processing
  • Improved credit access
  • Elimination of algorithm bias

According to McKinsey, AI is set to generate value above $1 trillion annually in the banking industry.

AI in Finance FAQs

AI in Finance FAQs

Will AI Replace Humans?

Most definitely not. The truth of the matter, however, is that AI will replace redundant and menial jobs. Some careers and jobs are bound to be wiped out by AI.

However, a recent report by WEF reveals that AI will eliminate 85 million jobs by 2025 but will also create 97 million new jobs at the same time. Therefore, this calls for reskilling and upskilling to avoid being rendered obsolete by AI and to prepare for the new era of work.

Is AI in Finance Safe?

Although there are some risks involving ethics, data protection, regulations, security, governance, and transparency, financial institutions have to minimize such risks and mitigate the impact if they occur. Thus, it is wise enough to conclude that AI in finance is safe.

How Accurate Is the Application of AI in Banking?

AI and ML technologies are built upon data that uses algorithms to analyze data and make predictions based on that information. Even though it is difficult to achieve 100% accuracy, AI in banking provides much higher accuracy than traditional banking can.

The beauty of it all is that AI is always self-improving and self-learning—so its accuracy and efficiency levels are constantly increasing. AI in banking enables more accurate fraud detection, bookkeeping, credit evaluation, and risk assessment than traditional banking can.

Companies are no longer just collecting data. They’re seeking to use it to outpace competitors, especially with the rise of AI and advanced analytics techniques. Between organizations and these techniques are the data scientists – the experts who crunch numbers and translate them into actionable strategies. The future, it seems, belongs to those who can decipher the story hidden within the data, making the role of data scientists more important than ever.

In this article, we’ll look at 13 careers in data science, analyzing the roles and responsibilities and how to land that specific job in the best way. Whether you’re more drawn out to the creative side or interested in the strategy planning part of data architecture, there’s a niche for you. 

Is Data Science A Good Career?

Yes. Besides being a field that comes with competitive salaries, the demand for data scientists continues to increase as they have an enormous impact on their organizations. It’s an interdisciplinary field that keeps the work varied and interesting.

10 Data Science Careers To Consider

Whether you want to change careers or land your first job in the field, here are 13 of the most lucrative data science careers to consider.

Data Scientist

Data scientists represent the foundation of the data science department. At the core of their role is the ability to analyze and interpret complex digital data, such as usage statistics, sales figures, logistics, or market research – all depending on the field they operate in.

They combine their computer science, statistics, and mathematics expertise to process and model data, then interpret the outcomes to create actionable plans for companies. 

General Requirements

A data scientist’s career starts with a solid mathematical foundation, whether it’s interpreting the results of an A/B test or optimizing a marketing campaign. Data scientists should have programming expertise (primarily in Python and R) and strong data manipulation skills. 

Although a university degree is not always required beyond their on-the-job experience, data scientists need a bunch of data science courses and certifications that demonstrate their expertise and willingness to learn.

Average Salary

The average salary of a data scientist in the US is $156,363 per year.

Data Analyst

A data analyst explores the nitty-gritty of data to uncover patterns, trends, and insights that are not always immediately apparent. They collect, process, and perform statistical analysis on large datasets and translate numbers and data to inform business decisions.

A typical day in their life can involve using tools like Excel or SQL and more advanced reporting tools like Power BI or Tableau to create dashboards and reports or visualize data for stakeholders. With that in mind, they have a unique skill set that allows them to act as a bridge between an organization’s technical and business sides.

General Requirements

To become a data analyst, you should have basic programming skills and proficiency in several data analysis tools. A lot of data analysts turn to specialized courses or data science bootcamps to acquire these skills. 

For example, Coursera offers courses like Google’s Data Analytics Professional Certificate or IBM’s Data Analyst Professional Certificate, which are well-regarded in the industry. A bachelor’s degree in fields like computer science, statistics, or economics is standard, but many data analysts also come from diverse backgrounds like business, finance, or even social sciences.

Average Salary

The average base salary of a data analyst is $76,892 per year.

Business Analyst

Business analysts often have an essential role in an organization, driving change and improvement. That’s because their main role is to understand business challenges and needs and translate them into solutions through data analysis, process improvement, or resource allocation. 

A typical day as a business analyst involves conducting market analysis, assessing business processes, or developing strategies to address areas of improvement. They use a variety of tools and methodologies, like SWOT analysis, to evaluate business models and their integration with technology.

General Requirements

Business analysts often have related degrees, such as BAs in Business Administration, Computer Science, or IT. Some roles might require or favor a master’s degree, especially in more complex industries or corporate environments.

Employers also value a business analyst’s knowledge of project management principles like Agile or Scrum and the ability to think critically and make well-informed decisions.

Average Salary

A business analyst can earn an average of $84,435 per year.

Database Administrator

The role of a database administrator is multifaceted. Their responsibilities include managing an organization’s database servers and application tools. 

A DBA manages, backs up, and secures the data, making sure the database is available to all the necessary users and is performing correctly. They are also responsible for setting up user accounts and regulating access to the database. DBAs need to stay updated with the latest trends in database management and seek ways to improve database performance and capacity. As such, they collaborate closely with IT and database programmers.

General Requirements

Becoming a database administrator typically requires a solid educational foundation, such as a BA degree in data science-related fields. Nonetheless, it’s not all about the degree because real-world skills matter a lot. Aspiring database administrators should learn database languages, with SQL being the key player. They should also get their hands dirty with popular database systems like Oracle and Microsoft SQL Server. 

Average Salary

Database administrators earn an average salary of $77,391 annually.

Data Engineer

Successful data engineers construct and maintain the infrastructure that allows the data to flow seamlessly. Besides understanding data ecosystems on the day-to-day, they build and oversee the pipelines that gather data from various sources so as to make data more accessible for those who need to analyze it (e.g., data analysts).

General Requirements

Data engineering is a role that demands not just technical expertise in tools like SQL, Python, and Hadoop but also a creative problem-solving approach to tackle the complex challenges of managing massive amounts of data efficiently. 

Usually, employers look for credentials like university degrees or advanced data science courses and bootcamps.

Average Salary

Data engineers earn a whooping average salary of $125,180 per year.

Database Architect

A database architect’s main responsibility involves designing the entire blueprint of a data management system, much like an architect who sketches the plan for a building. They lay down the groundwork for an efficient and scalable data infrastructure. 

Their day-to-day work is a fascinating mix of big-picture thinking and intricate detail management. They decide how to store, consume, integrate, and manage data by different business systems.

General Requirements

If you’re aiming to excel as a database architect but don’t necessarily want to pursue a degree, you could start honing your technical skills. Become proficient in database systems like MySQL or Oracle, and learn data modeling tools like ERwin. Don’t forget programming languages – SQL, Python, or Java. 

If you want to take it one step further, pursue a credential like the Certified Data Management Professional (CDMP) or the Data Science Bootcamp by Springboard.

Average Salary

Data architecture is a very lucrative career. A database architect can earn an average of $165,383 per year.

Machine Learning Engineer

A machine learning engineer experiments with various machine learning models and algorithms, fine-tuning them for specific tasks like image recognition, natural language processing, or predictive analytics. Machine learning engineers also collaborate closely with data scientists and analysts to understand the requirements and limitations of data and translate these insights into solutions. 

General Requirements

As a rule of thumb, machine learning engineers must be proficient in programming languages like Python or Java, and be familiar with machine learning frameworks like TensorFlow or PyTorch. To successfully pursue this career, you can either choose to undergo a degree or enroll in courses and follow a self-study approach.

Average Salary

Depending heavily on the company’s size, machine learning engineers can earn between $125K and $187K per year, one of the highest-paying AI careers.

Quantitative Analyst

Qualitative analysts are essential for financial institutions, where they apply mathematical and statistical methods to analyze financial markets and assess risks. They are the brains behind complex models that predict market trends, evaluate investment strategies, and assist in making informed financial decisions. 

They often deal with derivatives pricing, algorithmic trading, and risk management strategies, requiring a deep understanding of both finance and mathematics.

General Requirements

This data science role demands strong analytical skills, proficiency in mathematics and statistics, and a good grasp of financial theory. It always helps if you come from a finance-related background. 

Average Salary

A quantitative analyst earns an average of $173,307 per year.

Data Mining Specialist

A data mining specialist uses their statistics and machine learning expertise to reveal patterns and insights that can solve problems. They swift through huge amounts of data, applying algorithms and data mining techniques to identify correlations and anomalies. In addition to these, data mining specialists are also essential for organizations to predict future trends and behaviors.

General Requirements

If you want to land a career in data mining, you should possess a degree or have a solid background in computer science, statistics, or a related field. 

Average Salary

Data mining specialists earn $109,023 per year.

Data Visualisation Engineer

Data visualisation engineers specialize in transforming data into visually appealing graphical representations, much like a data storyteller. A big part of their day involves working with data analysts and business teams to understand the data’s context. 

General Requirements

Data visualization engineers need a strong foundation in data analysis and be proficient in programming languages often used in data visualization, such as JavaScript, Python, or R. A valuable addition to their already-existing experience is a bit of expertise in design principles to allow them to create visualizations.

Average Salary

The average annual pay of a data visualization engineer is $103,031.

Resources To Find Data Science Jobs

The key to finding a good data science job is knowing where to look without procrastinating. To make sure you leverage the right platforms, read on.

Job Boards

When hunting for data science jobs, both niche job boards and general ones can be treasure troves of opportunity. 

Niche boards are created specifically for data science and related fields, offering listings that cut through the noise of broader job markets. Meanwhile, general job boards can have hidden gems and opportunities.

Online Communities

Spend time on platforms like Slack, Discord, GitHub, or IndieHackers, as they are a space to share knowledge, collaborate on projects, and find job openings posted by community members.

Network And LinkedIn

Don’t forget about socials like LinkedIn or Twitter. The LinkedIn Jobs section, in particular, is a useful resource, offering a wide range of opportunities and the ability to directly reach out to hiring managers or apply for positions. Just make sure not to apply through the “Easy Apply” options, as you’ll be competing with thousands of applicants who bring nothing unique to the table.

FAQs about Data Science Careers

We answer your most frequently asked questions.

Do I Need A Degree For Data Science?

A degree is not a set-in-stone requirement to become a data scientist. It’s true many data scientists hold a BA’s or MA’s degree, but these just provide foundational knowledge. It’s up to you to pursue further education through courses or bootcamps or work on projects that enhance your expertise. What matters most is your ability to demonstrate proficiency in data science concepts and tools.

Does Data Science Need Coding?

Yes. Coding is essential for data manipulation and analysis, especially knowledge of programming languages like Python and R.

Is Data Science A Lot Of Math?

It depends on the career you want to pursue. Data science involves quite a lot of math, particularly in areas like statistics, probability, and linear algebra.

What Skills Do You Need To Land an Entry-Level Data Science Position?

To land an entry-level job in data science, you should be proficient in several areas. As mentioned above, knowledge of programming languages is essential, and you should also have a good understanding of statistical analysis and machine learning. Soft skills are equally valuable, so make sure you’re acing problem-solving, critical thinking, and effective communication.

Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!

About Sakshi Gupta

Sakshi is a Managing Editor at Springboard. She is a technology enthusiast who loves to read and write about emerging tech. She is a content marketer with experience in the Indian and US markets.