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Examining AI Uses in Banking & Financial Services

ai-uses-in-banking

The fusion of finance and technology has led to the emergence of an entire new industry – fintech. Driven by artificial intelligence and nascent technologies, fintech simplifies and automates financial operations. Consumers can now manage their finances with just a few smartphone clicks. Understandably, established banking institutions are getting increasingly serious about the use of AI in the banking and financial industry.

Customers increasingly recognize the advantages of fintech solutions as well. Over 61% of Americans are now using digital banking services, and the global value of mobile payments is expected to exceed $1 trillion by the end of this year. Yet, AI opportunities in banking are not limited solely to mobile services.

In this article, we will evaluate the benefits of using AI in the banking sector and examine its current applications. To learn how to implement AI in banking, read on.

The benefits of using AI in banking

AI has the potential to transform just about every existing industry, yet, in the banking and financial sector it can truly unleash its transformative potential. Below, is a list of some tangible benefits that AI applications can bring to the table in banking.

Enhanced customer service

Millenials hate banks! This is old news, but back in 2014 these survey results came as a surprise. Traditional banking has failed to meet almost all the expectations of today’s youngest and the most dynamic generation: the call for simplicity and fast time-to-value.

Fintech transformation and AI adoption in banking has opened the door for many opportunities. Chatbots and personal assistants save time and help find answers to some of the most frequently asked questions, now that there is no longer any need to engage in conversations with personnel and visit a physical bank.

Moreover, AI-based assistants help develop individual financial plans and make managing personal finances fun and entertaining. No need to wait in queues and try to understand the fine print, for that matter.

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Reduced workloads

Apart from the obvious customer service transformation, a quiet revolution is happening in back-office operations. Increasingly, robotic process automation (RPA), the integral part of a larger AI concept, is helping automate repetitive rule-based processes, streamline tiresome and meticulous routine tasks and avoid mistakes and fallacies.

By evading the need to open new positions, financial companies optimize their salary expenses. Existing personnel, on the other hand, can grow and develop in more creative ways.

Advanced data analytics

Insights derived from massive data sets enable banking and financial institutions to access a wide range of customer-related data and offer personalized and precisely targeted solutions. Advanced data analytics are the main driver behind the entire new branch of fintech — insurtech, i.e. the use of technological solutions to offer personalized insurance products.

 

Read more: Software Solutions for Insurance Companies: Best Practices

 

Better decision-making

In banking, AI analytics help arrive at better, data-driven decisions and account for improved operational efficiency and increased revenues. Marketing strategies are no longer based on guessing games: big data analytics have introduced precise targeting for each user group. Because of legacy infrastructures and processes, established banking institutions used to lag seriously behind fintech startups in terms of data analytics implementation.

With the advent of advanced digitization technologies helping transfer their historic data into a readable and processable format, their vulnerability could be turned into a major advantage. The interdependencies and patterns they are going to discover will give them an unquestionable competitive edge.

In trading, the use of AI is rapidly gaining the upper hand over traditional methods. Also known as high-frequency, quantitative or algorithmic trading, data-driven investments reached an astounding figure of $1 trillion in 2018.

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AI-driven risk management

Arguably, there is no industry like the financial industry equally dependent on external factors. Current or upcoming worldwide currency fluctuations, natural disasters and political unrest have serious impacts of banking operations and, ideally, should be taken into account when making business-decisions. Here’s where AI-driven analytics come into play, as they can also help prepare for the unexpected.

As of today, AI is frequently finding application in loan management, helping evaluate the probability of a client failing to pay back a loan. By analyzing past behavioral patterns and smartphone data, AI analytics help predict future behavior and decide on the loan eligibility of a particular client.

Fraud prevention

Last but not least, fraud detection using AI in banking is one of the most vivid examples of how artificial intelligence is disrupting financial services. Big data is characterized by high velocity, volume and value; by capturing and processing it in real-time, NLP algorithms can detect inconsistencies and discrepancies and ensure fraud prevention.

Similarly, AI algorithms contribute to protecting customer data and are especially successful in preventing credit card fraud.

All of the above practical examples illustrate how to apply AI in banking. As of today, a lot of market players are adopting AI, and you could be the next to follow suit.

Examining current AI uses in banking and finance

Below are some of the most prominent use cases of AI in the banking and financial industry.

Chatbots and personalized customer service

Chatbots and personal assistants are making customer experiences more personalized and provide real-time customer support. They also account for improved customer engagement, and help companies collect feedback, generate leads and, ultimately, reduce staffing expenses.

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Learn more: Chatbot Apps: The Future of Customer Service

 

Example: Developed by Bank of America, Erica, an AI-driven chatbot, leverages natural language processing to recognize speech and read customer text messages. The chatbot performs financial advisor services formerly carried out by human personnel, as well as day-to-day transactions. The service is available 24/7, and works whenever customers need it. The chatbot is self-learning and gets more sophisticated over time, while dealing with increasingly complex queries.

Fraud prevention

Hackers and scammers are getting savvier, but ML algorithms successfully prevent identity theft and credit card fraud. While clearly distinguishing legitimate behaviors from fraudulent activities, machine learning algorithms evolve and can successfully stand up against previously unknown fraud schemes.

Example: Plaid is a vivid example of how banks are using AI for fraud detection. Developed by a San-Francisco based company, the platform connects applications with a client’s bank accounts, and works as a widget ensuring the security of each particular transaction. Powered by machine learning technology, its fraud detection algorithms carry out complex real-time analytics to verify authenticity and nip any fraudulent activity in a bud.

Process automation

RPA algorithms increase accuracy and operational efficiency, help avoid mistakes and human errors, and reduce costs by automating time-consuming repetitive tasks. On top of that, employees are able to focus on more complex processes requiring human involvement.

Example: JP Morgan Chase has harnessed RPA (robotic process automation) to execute routine data extraction, regulatory compliance verification and document capturing procedures and achieve an impressive acceleration of its cash management processes.

As of today, fintech companies successfully leverage RPA to boost transaction speed and power front and backend operations. RPA is now getting more complex and is transforming into cognitive process automation. For example, CoiN technology owned by JP Morgan Chase reviews documents and derives data from them much faster than humans can.

Card management system

Corporate and personal card management systems automatically handle processes and operations that formerly involved going to a physical bank.

Example: American Express if offering online and mobile card management tools for managing corporate accounts. Available 24/7 and accessible from everywhere, these tools enable corporate customers to conduct secure transactions, allocate corporate payments, cancel, suspend and replace cards, etc.  Interactive dashboards provide in-depth information on all existing accounts.

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Wealth management and portfolio management

Fintech solutions are helping manage personal finances in a fun and engaging way. Mobile banking enables customers to conduct fast and secure transactions without having to wait in queues at the bank, while wealth management apps help promote financial literacy and stay more organized.

Example: To attract the tech-savvy millennials and meet their demand for more personalized user experiences, financial giant Wells Fargo has created a mobile banking app “Greenhouse” for managing personal finances. The app will assist customers with savings, bills, and will provide financial insights on how to successfully combine day-to-day spending with long-term financial planning.

Investment management

No more complex calculations: digital financial advisors are now helping individual customers manage savings and make smart investments.

Example: Weatherfront is a financial advice engine helping customers make smarter financial decisions and save for the future more effectively. The app provides investment insights based on professional financial management and advisory services and aims to “democratize access to sophisticated investment products”. The service offers customers a high-interest cash account for all kinds of savings, “passive investing” tools automatically look for investment opportunities, and advises customers on their retirement options, in which neighborhood they can buy a home, etc.

Investment prediction

No more guessing games: predictive analytics account for making data-driven decisions. Understandably, big data analytics help identify market fluctuations earlier and more accurately than legacy investment models.

Example: Finbrain, is a financial prediction service, leveraging Deep Learning algorithms to deliver precise predictions on stocks, currencies, and commodities. By capturing and processing real-time data from financial markets across the world, the service delivers accurate predictions and assists in making better financial decisions.

investment-prediction

Adopting AI To Power Your Business: A Step-by-Step Plan

These are some of the most prominent examples of AI in fintech. As you can see now, digital transformation is disrupting just about every aspect of the multi-faceted financial industry, with AI solutions enabling corporate clients and individuals to approach their finances in smarter ways.  If you’re wondering how to implement AI in your business, here’s a step-by-step plan for you to follow:

1. Look at your competitors

Even if you are delivering custom-tailored niche services, you’re probably not the only one in your industry targeting the same customers. Take a look at fintech solutions your competitors have implemented to have a clear picture of what’s already available on the market.

2. Identify the problem

Every single fintech product mentioned above offers tangible solutions to existing problems. Identify which problem or range of problems you want your AI service to resolve before you proceed.

3. Identify repetitive processes

Highly repetitive rule based processes are usually the ones accounting for a large number of mistakes, inaccuracies, and bottlenecks if performed by humans. Outline which processes in your business call for automation.

4. Assess potential business value

The cost of implementing AI-based solutions will depend on a lot of variables, but is usually quite high. Know the potential business value, to ensure the outcome justifies the expenses.

5.  Access the available resources

AI adoption in banking is a resource-intensive process requiring time, investment, and, most importantly, talent. Fintech developers with expertise in both finance and technology are rare. The sooner you recognize the internal capability gap, the better prepared you will be for the next logical step.

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6. Choose a reliable digital transformation partner

Partnering with a reliable digital transformation company and choosing the right one among the plethora of options is an important step.  Make sure the company you forge a partnership with has extensive experience in implementing transformation and innovation programs and has access to a vast pool of multifaceted tech talent.

7. Embark on your AI transformation journey

Don’t rush through it. Choose a small area of your business you can automate, and focus on its improvement. Starting with basic but high-impact tasks and processes that call for automation will help you implement AI solutions without disrupting your company’s ecosystem.

AI use cases in the banking industry speak for themselves. The obvious business value is there, but the risks are also high. As of today, not implementing AI in the banking and financial industry is almost as risky as implementing it incorrectly, that is, without taking into account the specifics of your company and the business environment.

To identify your business-specific areas for AI implementation and develop a detailed agile transformation action plan, contact our digital transformation consultants now for expert advice on AI adoption.

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