AI solutions for banks
AI has been a subject of discussion for many years now, but it’s only in the past decade that its potential use in the banking sector has really started to be explored. In this article, we look at some key aspects of AI as used by banks and financial institutions, including top use cases, AI-based solutions available to them, and notable examples of successful implementation. We will also discuss how AI (artificial intelligence) is being used by software developers today to improve not just efficiency and accuracy but also convenience for users—and make banking secure and user friendly.
Author: Paul Shumskiy
Last updated November 30
AI Overview: History of AI
Artificial Intelligence, or AI, weaves a rich history that begins in the 1940s. What started as cybernetic studies gave us invaluable advancements like binary logic and computer technology. These elements form the bedrock of what we now know as AI.
In 1950, Alan Turing opened new vistas with his work ‘Computing Machinery and Intelligence.’ He introduced the Turing test, a cornerstone in understanding artificial intelligence’s potential. Two years later, Arthur Samuel further pushed the boundaries by creating an intelligent self-learning game (Samuel Checkers-Playing Program), which was a first of its kind.
The term’ artificial intelligence’ gained its popular hold from John McCarthy’s workshop at Dartmouth in 1955. From then on until mid-1974, AI enjoyed incredible growth; faster computers with more storage capacity championed this evolution.
We saw Eliza – a chatbot capable of simulating conversation with humans – come alive during the vibrant 1960s. This decade we have also witnessed the birth of Shakey, the first mobile intelligent robot.
AI hit a roadblock between the late 70s and early 80s due to decreased research funding, among other challenges, causing what is now known as AI winter. However, this didn’t stop progress for long – experts emerged, introducing new applications for Artificial intelligence, again gaining interest within scholars’ circles, leading to a sort of renaissance period for it.
Finally came the revolutionary decade of the ’90s, where speech processing capabilities were added to machines whilst video processing underwent major improvements!
In the year 2000, Professor Cynthia Breazeal gave us Kismet, the very first robot that could show human emotions on its face. Then in 2006, leading companies like Twitter and Facebook embraced AI to enhance user experience by polishing their advertising strategies.
There were exciting moments in gaming as well, thanks to Microsoft, who brought out Xbox Kinect in 2010, letting gamers control games just by body movements. IBM wasn’t far behind either when it developed Watson—an NLP computer programmed for solving questions—so good it won a game of Jeopardy against champions in a televised contest!
2011 was special, too, because Apple introduced Siri, which since then has become synonymous with virtual assistants globally!
Fast forward to 2019, and Google’s AlphaStar made waves, reaching Grandmaster status and playing Starcraft2! Heading into the future, OpenAI began testing GPT-3 in 2020—a model that crafts code, verse, and writing tasks so proficiently you would think they are penned down by humans themselves.
In 2021 DALL-E from OpenAI was released, which got us closer to creating deep fake and realistic images via its precise picture captions. In 2022, OpenAI released ChatGPT (their most powerful AI model to date, including GPT 3 and 4), which took the world by storm, gaining more users in its first week than what Facebook and other social platforms gained in their first week of release.
After ChatGPT, every industry began to incorporate AI in their processes for better performance and results; this also led to massive investments being made in AI by other larger corporations such as Google, Microsoft, Apple and others.
Exploring the Essential Elements of Artificial Intelligence
Essentially, an AI system has two core components that allow it to do wonders.
First up is Machine Learning (MI), which is AI’s ability to learn from data and enhance its performance with time. Now imagine teaching your computer to recognize patterns just like you would teach a child A-B-Cs. Machines involve working on algorithms and statistical models so computers can perform tasks better without needing constant instructions.
Machine learning comes in several forms, such as supervised learning, where machines are given targets to achieve, or unsupervised learning, where they have no specific goal but gather information independently nonetheless. Another type includes reinforcement learning, which uses rewards or punishment approaches similar to training pets.
Another fundamental part of AI systems involves using logical rules along with probabilistic models and complex mathematical formulas known simply as ‘algorithms’ - all this helps AI draw conclusions based on available data.
AI works by understanding the world in ways similar to how humans do but with artificial sense organs. For example, when AI looks at a photo or video, it can pick out specific objects just like you and I would do. That bit of tech magic is called image or perception recognition.
But that’s not all: Another important part of this smart tool kit involves solving problems by analyzing challenges faced within different scenarios thrown at it – sort of playing detective!
Another component of AI is called natural language processing (NLP). Think about having a chat with your buddy on the phone; NLP allows an AI system to have those kinds of human conversations. It’s what helps power things like voice recognition systems or online translation services.
All of these five components - Machine Learning, Algorithms, Problem Solving, Perception Recognition and Natural Language Processing form the basics of an AI model, making it one of the most advanced technological companions yet.
Top Use Cases for Banks
Business Insider reports that banks are now more focused on artificial intelligence and its benefits than ever. About 80 percent of all banks recognize how AI could transform them, offering ways for both savings and profit.
In 2023, it’s expected that this technological adoption will aid banks in saving up to a massive $447 billion! To break these numbers down, an efficient AI application can help cut costs while boosting service quality and productivity at the same time.
The Economist Intelligence Unit shares additional insights. Their survey found that nearly 77 percent of bankers hold faith in AI as a deciding factor for triumph or failure in the industry.
A McKinsey study conducted in 2021 affirmed these changing attitudes and practices. It checked to see if at least one area within most organizations applies AI technologies already - with around 56% reporting positively!
So what could some of these applications actually be? Things like chatbots deployed by banks are helping customers every day. Banking apps powered by machine learning optimize financial operations, too. And let’s not overlook fraud detection systems using advanced algorithms increasing security measures vastly.
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How AI Chatbots are Reshaping Customer Interactions
AI chatbots are starting to play a key role in the banking sector. These chatbots work around the clock and provide accurate answers to customer queries, providing them with an improved customer service experience. Banks have also started using AI in their mobile apps for both Android and iOS platforms.
These intelligent bots learn from user behavior through machine learning algorithms. By doing so, they unearth valuable insights based on search patterns, which banks can then use to offer personalized services or recommendations.
Due to this fact, about 70% of banks are looking into integrating AI into their mobile banking technology. This is seen as a step toward harnessing the full potential of artificial intelligence’s benefits for the banking industry.
For customers, it’s like having a personal banker at your fingertips at all times. With chatbot assistance, you can keep an eye on your account balances, check past transactions, and even access other account related information whenever required.
Chatbots further simplify financial tasks such as transferring funds between accounts or making payments to merchants, among others. Even steps involved in applying for loans can be guided systematically by these virtual assistants provided by different banks.
AI’s Role in Fraud Detection and Credit Assessment
Fraud is a big problem for banks. It causes them to lose lots of money every year in activities like identity theft, credit card fraud, and money laundering. AI can help banks in detecting fraudulent transactions and can make the banking system secure.
AI can analyze large amounts of data very quickly. This allows banks to pinpoint suspicious activity quickly and stop it before they lose any more money.
But this isn’t the only area where AI helps in banking. Another critical area is developing credit scores for potential borrowers.
Banks have to be sure about a person’s ability to repay loans before giving out money, but often, human biases get involved, which are not accurate at times. That’s when AI comes into action again by analyzing lots of data on spending habits, credit history, even things like social media use and location information.
AI-Driven Marketing Strategies in Banking and Wealth Management
AI can also help banks in digital marketing and also play a role in the wealth management of their clients or customers. For instance, in their attempt to boost fortunes at the front-office level, many banks have started applying AI-driven technologies. The goal is to understand what potential customers want and reach them effectively.
AI capabilities can be used to track a user’s actions online, like website browsing patterns or social media habits. This data helps draw up marketing plans tailored precisely for each consumer. Moreover, it can be used to not just set up campaigns but also assess their performance by studying conversion rates and other metrics. Here again, large amounts of raw data play the lead role – collected from different platforms where ad impressions get recorded for analysis by different algorithms.
Next comes wealth management – an area that has experienced sizable shifts due to AI’s imprint. Banks use intelligent systems that examine market mood swings closely along with specific events that might affect stock prices, such as economics or politics, even weather changes!
Through analyzing all of this data, AI can be used to provide customer specific recommendations that generate profits for individual users as well as investing firms and banks.
The Use of AI in Different Banking Sectors
A report by The Economist highlighted the major sectors where banking systems make the most use out of AI. The first is fighting fraud, with about 58% of the banks using AI in a great measure and another 32% using it moderately.
The second major task is optimizing IT operations, where roughly 54% of the banks use AI intensively, while around 36% make light but notable uses. Interestingly, AI is used across almost all business areas within the banking sector, from regular operations to enhancing customer experience.
Looking into the future, certain trends begin appearing as priorities for employing AI in banking services. Data suggests that nearly 17% of banks will take steps towards the adoption of personalized investment strategies aided by AI in the near future, followed by approximately 15% applying it to credit score improvements, and another 13 % will be looking at Portfolio Optimization prospects.
Top AI Solutions for Banks and Fintechs
In our modern world, the finance sector explores new methods for enhancing operations. One such method is the utilization of different AI softwares by fintech companies and banks. Some of these applications include:
Booke harnesses artificial intelligence to streamline bookkeeping tasks within financial teams. With Booke at hand, vexing issues like uncategorized transactions or coding errors no longer haunt accountants’ spreadsheets.
Month-end closing of books doesn’t need to be tedious anymore with Booke. It automates the majority of it while also tidying up any previous mess-ups unnoticed by human eyes.
The interaction between clients and financial companies can significantly improve with Booke’s easy-to-use portal. It changes communication from monotonous email exchanges into real-time problem-solving activities. Over time, as it learns more about company-specific transactions, categorizing them accurately becomes its forte, with over 80% faster transaction categorization. Booke melds seamlessly with popular accounting platforms like Xero, QuickBooks, and Zoho Books, providing versatile two-way integrations without a hitch.
Finally, it makes data extraction from digital receipts easy, even when there is a lot of data that needs immediate attention.
Erica is a game-changing AI software that was launched by Bank of America (BofA) in 2018 and has been crucial in providing support to their customers.
Erica is much more than just an online banking assistant. She has been instrumental to over 32 million people and has managed over one billion interactions so far! Countless customers rely on Erica daily for their banking needs. From checking account balances to making transactions, Erica handles it all with ease.
Now, BofA plans to take this interaction even further as they enhance Erica’s capabilities. Starting from early 2023, users were able to switch between chatting with the virtual assistant and speaking with a real human agent whenever necessary. The smooth transition ensures that every pressing issue finds resolution by expert hands.
What really sets Erica apart? It’s her built-in natural language processing, which lets her predict why a user might need help even before they say it! With insights gathered through these interactions, real-life agents can then provide customized product recommendations for clients, enhancing the overall customer experience significantly.
Interestingly enough, though, BofA found this tool isn’t only popular among younger generations but older adults, too, as they are also embracing this technological revolution!
AI is also driving innovation within investment platforms and providing services that fintech companies and banks are using. Take Robinhood for example; this popular trading app uses AI to stand out from its competitors. It offers personalized investment opportunities based on user preferences, like willingness to take risks and investing history.
Then there’s also a new app that goes by the name Magnifi. Leveraging programs such as ChatGPT, it provides tailor-made investment advice, much like how programmers use codes. Magnifi acts as a personal stock advisor and is also a trading platform – all rolled into one.
But AI doesn’t stop at investments alone; it is also being utilized in providing lending services. Platforms such as Upstart and C3.AI help lenders approve more borrowers while reducing the risk for everyone involved. C3.AI has even claimed their smart lending technology can streamline the credit process, cutting down approval times by 30%! To back this up, the platform has already gained a $100 million increase in loan acceptance yield and acceptance volume.
Nanonets Flow simplifies finance tasks and empowers professionals to grow their businesses. It does this by automating complex processes, making time spent on mundane tasks such as data entry to be available for something else like devising financial strategies.
The tool can also read important details from documents like invoices and bank statements, eliminating manual errors. Beyond gathering data, Nanonets Flow also manages workflows. It plugs smoothly into existing systems, suiting financial companies perfectly well.
The utility of Nanonets is vouched for by its developers on the Nanonets website, claiming that it processes invoices 10 times faster than manual means along with having ‘No extra charges for Automated Clearing House or card payments.’
AI-powered Banks and Fintechs
The advent of AI has brought with it many benefits that haven’t gone unnoticed by major industry leaders. Banks and other Fintech companies have been quick to realize their potential and have been making constant efforts to make full use of AI in their operations. Banks and Fintech companies that have done so include:
Capital One developed Eno in 2017. This artificial intelligence tool can be accessed via a mobile app, desktop or through simple text messages and emails. Eno helps users ask questions about their accounts.
It also alerts them of any fraudulent activities. It makes banking tasks simple, like paying credit cards and checking balances. The interesting part is Eno uses emojis to communicate just like we do.
One customer described it as “It is very easy to use and convenient. Saves from having to call”, while another said, “I love not having to log into my account, and with a quick text, can get info abt my account or even make a payment.”
Ally is another AI application from Ally Financials that has been associated with the banking industry for over a century now. Banks are now integrating this AI into their mobile apps, which boasts of a smart chatbot. This chatbot helps customers with all their queries, be it related to fund transfers or payments. Plus, point - the chatbot understands spoken as well as written words!
Rhoda, a satisfied user, while giving it a five star rating, says, “I am 71 years old, and my youngest daughter told me about Ally Bank. I am so glad I took her advice. Thank you.”
Another 5 star customer review of the AI reads, “Our financial advisor suggested that we try Ally for our banking for a higher interest rate on savings. We did it!”
In the vast banking sector, ABN Amro of The Netherlands stands tall. This bank is taking a bold step ahead and experimenting with Generative AI technology.
Annerie Vreugdenhil, a key executive in the organization, shared insights recently. She gave CNBC peeks into how they’re using this new tech to uplift their service standards. One interesting application is automatic summaries of meetings between employees and customers.
This not only saves time but also offers an easy reference point for future interactions, eliminating redundant queries. Additionally, it’s helping staff compile customer-related data swiftly while dealing with client issues more effectively.
The exciting news is that ABN Amro plans to expand these trials involving about 200 individuals from its workforce. They are also planning on starting numerous other pilots during the summer period. Evidently, this innovative leap by ABN Amro sets an inspiring example for other institutions within the banking industry worldwide.
Goldman Sachs is also experimenting with AI and trying to incorporate it into their operations. The bank’s chief information officer, Marco Argenti, spoke about using generative AI tools within the corporation. These tools are no ordinary ones - they help developers create code effortlessly, saving them valuable time. But that’s not all; in May this year, Goldman launched a new startup called Louisa from its private incubator hub. What’s interesting here? Louisa is driven entirely by artificial intelligence! This move signals CEO David Solomon’s ambitious plan to speed up Goldman Sachs’s digital transformation.
Back in 2017, JPMorgan Chase took a step into the future with an investment in technology; they introduced a Contract Intelligence (COiN) ‘chatbot.’ This tool analyzes legal documents to extract important data points and clauses. Before COiN, it took around 360,000 man-hours to review about 12,000 credit agreements yearly. But with this chatbot, these can now be reviewed in just seconds!
This successful experiment has prompted the company to explore new areas where they could put COiN to use. The details on further implementation remain limited for now.
Now shifting gears towards Wells Fargo, which remained somewhat quiet about its AI plans until Steve Ellis, head of Innovation Group, issued a press release statement back in 2017. They were piloting a chatbot created by AI vendor Kasisto on Facebook Messenger that was accessible initially only to certain customers and employees.
Having scored well on Emerj’s AI Opportunity Landscape research, it could be argued that Wells Fargo’s chatbot was among the best out there, but whether this got expanded across all customers remains unclear to date.
Lucinity, an Icelandic regulatory technology company, is also making use of AI in a remarkable way. Gudmundur Kristjansson, the CEO and co-founder of the firm, shared interesting insights with CNBC about how AI aids in combating financial crime.
To support compliance professionals in their investigations, Lucinity has designed Luci. This smart tool is revolutionizing how inquiries are conducted into finance-related crimes such as money laundering. Kristjansson demonstrated this by investigating a live case of money laundering through Luci.
Kristjansson further remarked, “Where you find money laundering is through ... interconnected networks of people who are basically employed to do it. That’s why it’s so hard to find it.”
This advanced tool doesn’t just assess data but also independently verifies and reviews it. It serves as a copilot to those working on unraveling fraudulent transactions or suspicious activities rather than replacing human input altogether. Its strength lies in saving time spent on deciphering whether a transaction involves fraud or money laundering.
Morgan Stanley is also another titan utilizing AI to enhance the productivity and efficiency of its employees. Their method seems more focused on internal use, where they have tested an OpenAI-powered chatbot with 300 advisors so far. The purpose behind this strategy is simple yet powerful: to aid approximately 16,000 advisors to swiftly access information from their extensive research data repository when it is needed the most.
In simple terms, thanks to the incorporation of AI in different sectors, we witness the unfolding era where artificial intelligence assists not only in streamlining operations but also aids significantly in ensuring security within industries such as banking and fintech.
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AI-Powered Banking Software Systems
Artificial intelligence (AI) is changing the world, one industry at a time. It’s playing a major role in many sectors, including banking. Banks are also taking advantage of this revolution and are making whole systems around different AI softwares.
NetOwl is an advanced AI software suite. It uses machine learning to power its Identity and Text Analytic products. These tools dissect big data, be it from documents or social media posts, as well as structured entity content.
One of Canada’s biggest banks, the Royal Bank of Canada (RBC), takes advantage of NetOwl’s capabilities in meaningful ways. To ensure compliance with banking regulations, RBC leans on NetOwl for detailed data analysis. Diverse forms of risk, especially those pertaining to threats, are monitored effectively using this system, too. Moreover, the quest for new customers by RBC carries lesser risks now due to the efficiency of NetOwl’s AI software known for identifying people linked with fraudulent activities in a timely manner.
This way, RBC saves itself and its investments from possible malicious actions that can drain resources while causing significant damages if overlooked.
Kasisto has an AI software that powers something called KAI. Kai isn’t your average platform. From complex bank processes to simple transactions, it is trained on everything that is required to run banking operations. Many big players like Mastercard and JP Morgan use Kasisto’s platforms. DBS Bank – a global player based in Singapore, shook hands with this technology by launching their award-winning digibank based on KAI.
Digibank functions solely on mobile operations across Indonesia and India - all thanks to KAI. However, that’s not all; anyone can access KAI anywhere through Facebook Messenger or on websites and even via its mobile App! Allowing you to ask endless questions and get accurate answers every single time.
Moven Enterprise crafted an AI-powered software platform system exclusively for banks. The project was a blend of high-tech artificial intelligence and the latest mobile banking tools. The fusion resulted in a platform that helped reduce both attrition rates and acquisition costs for customers while identifying new ways to make money.
The goal of Moven’s technology is clear. The firm seeks to provide adaptable, tailor-made banking solutions that can boost customer engagement, loyalty, and growth. Australia’s Westpac Bank and Toronto-Dominion Bank (TD) from Canada are reaping the benefits of this innovative approach.
Westpac teams up with Moven to use data analysis that enriches their customer experience significantly. They want it as a tool to become a top-of-the-notch digital bank. TD Bank’s MySpend app exemplifies Moven’s effectiveness well, too - users track spending habits with ease through real-time alerts about financial status updates.
Within 9 months since its launch, over eight hundred thousand people were registered into TD Bank’s MySpend app, says Rizwan Khalfan, the chief digital officer at TD Bank himself! To highlight success further, he notes how users have cut their expenditure by roughly 4-8% who use the App most frequently, seeing maximum impact.
The future looks increasingly bright for AI applications in banking — especially when combined with other cutting-edge technologies such as machine learning or natural language processing. Banks and fintechs can utilize these tools to provide better products to their clients while also utilizing data insights from customer interactions — ultimately leading towards an improved experience overall. Third-party software developers are also partnering up with different banks and organizations to custom-make AI platforms that are suitable to their needs. This approach continues to push boundaries through various releases discussed above — allowing customers more control over their funds than ever before, with unmatched ease and security.