AI in Banking: How Artificial Intelligence Elevates the Financial Sector
What does AI truly mean for your fintech business beyond the buzzwords and tech jargon? It signifies a shift in market dynamics, the promise of financial gains, and an avenue for enhanced client loyalty.
McKinsey’s AI Playbook announces, “With AI-based software solutions, businesses have the potential to unlock up to $1 trillion in extra value!”
JPMorgan Chase isn’t far behind, confidently projecting an ambitious $1.5 billion in value from AI by the year’s end.
But even if you’re operating in a more modest segment of the fintech market, understanding the implications and benefits of AI is important. We offer you to discover how AI can immediately serve as a catalyst for your growth.
Let’s get past the hype and directly engage with those at the heart of AI in Banking — the service providers and users.
There are endless hidden cost in any product and project-related lifecycle, but if I need to narrow it for product development precisely, these will be my picks.
Regulatory costs. You just need to follow changes in regulations and, if you are outsourcing, choose a company with strong domain expertise.
AI in Banking and Finance: The Dual Perspective of Users and Providers
Rapid advances in AI guarantee that soon it will be tightly interwoven into the fabric of our financial lives. Whether you’re a regular banking customer or a finance professional, AI is reshaping the way you interact with money.
Data-informed banking experience.Everything we do leaves a digital footprint. Every transaction, every click, tells a story. AI helps banks and financial institutions make sense of all this information, turning overwhelming amounts of data into smart decisions.
Personalization.Gone are the days of generic banking services. AI helps to design services tailored to your financial habits and goals, making your banking experience feel, well, more “you.”
Safety and assurance. The financial world has its share of pitfalls. AI serves as a vigilant guardian, predicting challenges and safeguarding against fraudulent activities. It is used in security testing, fraud prediction and assessment of potentially malicious users.
Efficiency at its best. The endless queues at banks and the long hold times on calls? Those are gradually becoming tales of the past, thanks to AI-driven automation ensuring faster and smoother operations.
Data-informed banking solutions. Digital finance world is full of data and analyzing that data in real-time comes with customers’ higher satisfaction and extra financial gains. AI helps providers to analyze vast amounts of data faster and more efficiently, find actionable insights, and offer services that are both user-centered and profitable.
New-level customer engagement.By understanding client behaviors, AI allows providers to offer solutions that resonate, building stronger relationships and ensuring customer loyalty.
Risk mitigation. AI models provide a clearer financial foresight, aiding providers to make more informed lending decisions and ensuring compliance.
Operational elevation. AI’s impact is evident across every aspect of banking operations. From automating mundane tasks to enhancing decision-making processes, from enhancing anti-money-laundering measures and anti-terror operations to streamlining back-office activities, AI plays a vital role at every level, according to SAS Insights.
Having touched upon the influence of AI in digital banking, let’s move onto the main event: a closer look at the specific AI tools that are revolutionizing banking practices.
Key AI Technologies in Banking with AI Use Cases
Here are some key AI in the banking industry technologies with examples of practical usage.
Machine Learning (ML).Algorithms that improve over time by using data. They “learn” from previous experiences to make better decisions.
Credit scoring. ML algorithms help in predicting which customers might default on their loans by analyzing past behavior and data patterns.
Natural Language Processing (NLP). Enables machines to understand and respond to human language. Almost every quality chatbot and voice assistant out there uses natural language processing.
Customer support.Chatbots like Bank of America’s Erica use NLP to understand and respond to customer queries in real time.
Robotic Process Automation (RPA). Software robots that perform repetitive tasks automatically, speeding up processes and ensuring accuracy.
Back-office. RPA bots can automate mundane tasks like data entry, speeding up account setup processes and reducing human error.
Predictive Analytics. Analyzes current and historical data to predict future outcomes. It is useful for both users and service providers, as it helps to make informed decisions.
Marketing. By analyzing spending patterns, banks can offer personalized financial advice or product recommendations to customers.
Voice Recognition and Biometrics.Technologies that identify individuals based on unique physical or behavioral traits, such as voice patterns or fingerprints.
Security and authentication. Some banking apps now allow users to log in using voice commands or fingerprint scans, ensuring a higher level of security.
Neural Networks and Deep Learning. Advanced forms of machine learning, where algorithms mimic the human brain’s structure and function to process data.
Fraud detection. Credit card fraud detection systems can employ deep learning to identify unusual transaction patterns, alerting both the bank and the cardholder of potential security breaches.
Blockchain and Distributed Ledger Technology (DLT). A secure, decentralized way to record transactions. It’s like a shared digital ledger that everyone can trust.
Cross-border payments and settlements.Beyond cryptocurrencies, banks must use blockchain to enhance the transparency and security of transactions, as seen with projects like J.P. Morgan’s Quorum.
These AI use cases are not exhaustive, though. Here are some more real-world examples of AI applications in banking big players rely on:
- Bank of America uses machine learning to detect fraud flagging unusual transactions and alerting customers to potential breaches.
- J.P. Morgan relies on NLP to analyze legal documents in seconds, a process that traditionally took human experts thousands of billed hours.
- Santander was one of the first banks to introduce a blockchain-based money transfer service, allowing customers to make international transfers within a day.
QA services for Financial Services Commission Mauritius
Financial Service Commission Mauritius is an integrated regulator for the non-bank financial services sector and worldwide business. It started the Online Submissions Platform (OSP) project to turn much of its ongoing paper-based application process into online submissions and to introduce online payments.Read more
Odoo Development For An Insurance Company
Developing an Odoo solution for an insurance company that wants to streamline the processes for its partners and make sure that insurance calculations are quick, precise, and fault-free.Read more
Loan Management System For Thrift Plus 1
Developing a web app for quick and easy loan management that can synchronize data across different servers and financial institutions and is based on our previously developed solution.Read more
Exploring the Possibilities: Applications of AI in Banking
We have just covered the core technologies powering the AI revolution in banking and finance sector. You might wonder why we’d circle back to the same territory. The reason is perspective.
While understanding the tech behind the scenes is crucial, there is a chance you are reading this article not with a distinct business purpose. You may be looking to develop a cutting-edge financial solution, deploy a new banking product, or enhance your existing system. So what you really want to know is how to align adoption of AI capabilities with specific banking products.
Know Your Customer (KYC)
Truly knowing your customer is both a regulatory mandate and a smart business move. AI makes the task less of a chore and more of a charm.
- Automated document verification. KYC typically involves verifying heaps of documents. Adopting AI can help swiftly scan, verify, and categorize these documents, ensuring that customer onboarding is both fast and compliant.
- Behavioral analysis for red flags. It’s not just about initial verification. AI can also monitor transactions to spot unusual behaviors or patterns. If Mr. Smith suddenly sends a huge sum overseas, AI suspects a “stolen credit card” case and raises the flag for further review.
- Facial recognition for live verification. With AI, a live video or selfie can cross-check faces with stored ID photos, offering an additional layer of verification that’s both user-friendly and secure.
- Continuous data update. People’s lives change – they move, get married, change jobs. AI can prompt customers to update their details, ensuring that the bank’s data is as current as possible. For providers, this means always staying compliant without the manual hustle.
We will circle back to some of these functions in the following operational and product aspects. Bear with us, as fintech is an interconnected web, where you can’t just fully separate KYC and fraud prevention functions and solutions.
Checking and Savings Accounts
Checking and savings accounts, not just a place to keep money. AI helps to turn these classical banking products into smart financial tools.
- Personal financial management. AI can analyze spending habits, categorize transactions, and offer budgeting insights, giving your banking mobile app a leg up on your competitors.
- Anomaly detection. Beyond the standard unusual activity alert, think of a system that learns a user’s spending habits over time. Red flags now can consist of making purchases in unusual hours, spending week-worth budget in an irrelevant, never visited before online-store, repetitive side service request for a verification transaction, services not consistent with the client’s spending style.
- Future spend predictions. Financial guru and wannabes advise people to budget as the first step to financial stability and freedom. In reality, it is close to advising “eating healthier” — generic and hard to follow consistently. A predictive model in your app could forecast a user’s balance at the end of the month based on their recurring bills and typical spending and help with better financial planning.
- Context-aware spending analysis. AI can detect the context and the anomalies, analyzing card transactions in relation to real-world events. For example, if there’s a spike in spending while you’re on vacation, the system understands the context, reducing false alerts for unusual spending while still monitoring for genuine fraud.
- Automated savings round-ups. By integrating AI, cards can help users save. Each transaction can be rounded up to the nearest dollar (or $10), with the difference automatically transferred to a savings account. Over time, AI can even suggest adjustable saving rates based on spending patterns and saving goals for a defined period.
- Adaptive card locking. AI can predict when you’re less likely to use your card — like during your regular sleeping hours — and auto-lock it to prevent any unauthorized access. In case of genuine transactions during these hours, it learns and adjusts.
Loans and Mortgages
With AI, financial institutions can provide a smoother, more informed borrowing experience, increasing chances to get loans even without great credit history and protecting lenders from malicious users.
- Seeing the full picture in credit scoring. AI helps banks look at more than a clients’ loan or credit history to determine their creditworthiness. It can consider things like your shopping habits, online activity, and more to get a better idea of how good you are with money. This means some people who might have been skipped over before could get a shot at a loan.
- Quick loan approvals. No one likes waiting. With AI, loan approvals can be almost instant, making customers happy about this particular financial institution and allowing banks to do more business thanks to the word of mouth marketing both online and offline.
- Spotting loan issues in advance. AI can help banks guess when someone might struggle to repay a loan, way before the person themselves realize it. This way, they can check in early and figure out a plan before things get messy.
- Fair play in lending. Sometimes, without even meaning to, people and systems have biases. This can lead to unfair loan decisions. AI can be trained to minimize these biases, ensuring everyone gets a fair shot.
Using AI for investment services in a banking product helps to ensure both fresh-faced and veteran investors stay on course.
- Tailored robo-advisors. AI-driven robo-advisors aren’t just smart; they’re intuitive. They learn about client’s financial goals and habits. This means customers get personalized advice, boosting trust and long-term loyalty to your bank or firm.
- Market pulse with Sentiment Analysis. AI solutions fish out valuable insights from news, social media, and financial forums. Offering this intel to your clients can give them an edge, positioning your service as a go-to source for market foresight.
- Automated portfolio management. Manual portfolio rebalancing feels so last decade. With AI, portfolios auto-adjust based on market trends. For providers, this means fewer human errors, increased efficiency, and a modern service that attracts tech-savvy investors.
Digital Wallets and Payment Systems
You and your clients constantly have money on the move. AI ensures this dance of digits is swift, secure, and smart.
- User payment predictions. AI observes and learns user habits. For banks and fintechs, this means anticipating client needs, and rolling out tailored financial products or timely offers.
- Route optimization for transactions. AI finds the quickest, most efficient path for transactions, cutting down wait times and increasing customer satisfaction.
- Smart promotions. Based on user spending and habits, AI applications can suggest the right deals and discounts at the right time, increasing transaction volumes.
If you want to roll out a separate trading solution or incorporate it in into the existing banking product, don’t overlook possible AI integrations.
- Automated document verification. Checking and cross-referencing trade documents is old school. AI speeds up the process, reducing errors and ensuring smoother trade operations.
- Trade risk forecasts. With AI’s analytical prowess, foreseeing potential trade hiccups becomes significantly easier. For providers, this means better risk management, ensuring your clients’ trust remains intact.
- Currency conversion. AI algorithms keep an eagle eye on currency fluctuations, ensuring optimal conversion rates. It’s about maximizing value with every transaction, making your financial platform the preferred choice for businesses.
- Algorithmic valuation. AI evaluates assets faster and makes accurate and current valuations based on a multitude of factors.
- Market movement forecasting. Instead of gut feelings or broad market trends, AI analyzes vast data to forecast precise market shifts.
- Automated asset allocation. Tailoring investment portfolios becomes hassle-free as AI auto-allocates assets based on specific strategies and goals.
Foreign Exchange and Remittances
- FX rate prediction. AI digs into past trends, current news, and more to predict potential FX rate movements.
- Remittance timing. By understanding the FX market’s ebb and flow, AI can suggest the most cost-effective times for international money transfers.
- Automated conversion. AI swiftly processes conversions, and users get the most bang for their buck, linking this speed and efficiency to the quality of your product.
Adopting AI in the Banking Sector: Future Prospects
The future of AI in banking lies at the intersection of advanced technology and consumer needs. As AI becomes more intertwined with finance, several groundbreaking trends are emerging.
Modernizing legacy systems. For banks to even consider using AI technology efficiently, they need to upgrade their core systems. A hybrid, multi-cloud strategy allows combining the best of AI’s scalability with the security of private clouds and on-site setups.
Rise of super apps. Traditional banking apps focused on specific functions. The future lies in super apps, single platforms integrating banking, shopping, payments, investments, and more. This consolidation offers customers convenience and streamlines operations for providers.
Generative AI.Beyond just analyzing data, AI will generate new data sets and models to simulate different financial scenarios. This could be crucial for risk assessments, allowing banks to predict and prepare for various market conditions.
Embedded finance. No longer a stand-alone sector, financial services will be deeply integrated into non-financial platforms. Think of checking out on an e-commerce site and getting instant loan offers for your purchase — all powered by AI.
AI-driven financial health checkup. Just as you have a health checkup, AI will offer financial checkups, analyzing your financial behaviors, predicting future challenges, and offering advice to keep you on track.
Blockchain and AI convergence.The secure nature of blockchain combined with the intelligence of AI can reshape financial operations, especially in fraud detection and international transactions.
Augmented Reality (AR) banking. AR could change the face of banking, literally. Envision checking your account balance or visualizing your spending patterns through AR glasses.
Open banking with AI. As more regions adopt open banking regulations, AI will play a crucial role in analyzing vast data from different financial institutions, offering users consolidated insights and tailored advice.
Ethical and transparent AI. With increasing scrutiny on AI’s decision-making processes, there’ll be a move towards more transparent algorithms, ensuring decisions are fair and free from biases.
By prioritizing the alignment of your technology roadmap with a comprehensive AI strategy and collaborating with software development vendors, banks may position themselves for success in the dynamic landscape of digital financial services.
Smarter Banking, Brighter Future: QArea’s AI Innovations at the Heart of Finance!
Deploy Your AI-First Bank App: In-House vs Outsourcing
Deciding between in-house or outsourced development for AI-powered banking apps should not be a roll of the dice. Here are some criteria you should consider when planning to use artificial intelligence in banking and finance products.
|Infrastructure, software, hiring, onboarding, training, retaining
|No need for infrastructure setup; pay for services rendered
|Given, proven by various cases
|Requires a separate hire through the full recruitment process, unless AI is a core company’s competency
|Dedicated teams with necessary expertise can start in two weeks after the initial request
|Flexibility and scalability
|Limited by the structure
|High and controlled by you
|Depends on size & skill set of in-house team, but often highly limited in rigid banking systems
|You can swiftly scale team up/down and request some of the experts changed no questions asked
|Training, salaries, benefits, software licenses
|Paying only for specific services and specialists
|Access to talent
|Limited by geography
|Wide (near-, on-, offshore)
|Bound to your geographical location and often overprices due to tax and social benefits reasons
|Access to a diverse range of experts in AI and supporting dev/QA technologies and languages
|Time to market
|Due to learning curves and potential resource constraints
|Expert teams with established workflows and domain experience
“If you have a legacy-based system in the US or UK to maintain, you are probably paying a stupid amount of money to do it. Train people in other countries, or hire an offshore/nearshore team at least 80% versed in what you do – the support costs will go down. Make sure such decisions are compliant with your SLA. When a company is starting up, they have limited resources and don’t have enough expertise. They may not have domain- or software-based backgrounds, so they need support and guidance. Reduced costs and additional expertise synergize when it comes to outsourcing your project to domain experts.”
Here are some examples of how financial services firms and banks can approach in-house vs outsourcing decision. The right choice that will allow reduce the time of development and QA, offering quality services to customers in a faster and more efficient fashion.
Bespoke fintech startup. Imagine a small startup with a unique idea tailored to a local market, relying on at least one AI capability to make it pop among the competition. They have a deep understanding of their client base, existing relationships with stakeholders, and specific nuances they want to capture in their app (for example, introducing local market specific ai-powered chatbots and virtual assistants). This situation leans towards in-house development, where the team’s domain knowledge, coupled with the proximity of in-house talent, can quickly iterate, get feedback, and refine the app to perfection.
Global bank expansion. A multinational bank is looking to expand its digital savings, transfer, loan and credit services to newer markets faster than their competitors. They aim to launch rapidly in multiple regions, with top-notch features powered by more than a few key applications of AI. Given the vast scope and the deadline, they’d benefit from outsourced development. Outsourcing grants them access to a pool of global talent, faster deployments, and the flexibility to scale teams per regional requirements without the constraints of their in-house teams.
Mid-sized bank revamp. Consider a mid-sized bank planning to overhaul its digital platform, integrating AI for personalized customer experiences covering income and expenses operations with better accuracy and speed. They have a dedicated IT team, large volume of data gathered and stored, but lack the AI expertise. In this scenario, a hybrid approach might be ideal. They can retain core functionalities in-house, ensuring alignment with their existing systems, while outsourcing the AI component to specialists, ensuring best practices and cutting-edge features.
The rapidly evolving digital financial services landscape has introduced significant opportunities for banks to use AI technologies and create applications that could drive better customer experiences, higher efficiencies, and greater insights.
By understanding the different AI tools, understanding where to invest efforts, and capitalizing on the advantages of outsourcing app development with a trusted partner, banks can capture value quickly while still meeting regulatory requirements. Amidst these rapid changes in fintech, it’s essential for businesses in the banking sector to stay ahead of the game and rely upon cutting-edge technologies like AI to build innovative financial services and products.
Investing time and resources into AI-driven banking software solutions is an intelligent move that could shape a competitive advantage in today’s strategic marketplace. If you’re looking for a partner who understands banking needs as well as technical expertise, look no further than QArea – your trusted solution when it comes to developing AI-powered solutions for banking!
Contact us to map out your AI software development journey, tailored to your project and guided by your rules.
How is AI used in banking?
There are various use cases of AI in banking, starting from the use of AI to improve efficiency and the use of AI to reduce the need for manual double-checking. AI is also used in banking to streamline operations, enhance customer experiences, and improve risk management. It powers chatbots and virtual assistants for personalized customer support, automates repetitive tasks through Robotic Process Automation (RPA), and enables predictive analytics for better risk assessment and fraud detection. Additionally, AI-driven algorithms analyze customer data to offer personalized financial products and services, transforming the way banks interact with their customers and manage their operations.
What benefits does AI bring to the banking industry?
Not all banking experts are aware of the potential benefits presented by AI. This technology brings numerous benefits to financial services companies. It enables banks to provide more personalized and efficient customer experiences, leading to higher customer satisfaction and loyalty. AI-powered systems can detect and prevent fraud in real-time, reducing financial losses and ensuring a more secure banking environment. Moreover, AI streamlines internal processes, improving operational efficiency and reducing costs. Overall, AI’s transformative impact empowers banks to make data-driven decisions, stay competitive, and adapt to the rapidly changing financial landscape.
Does AI in banking pose any risks or challenges?
While AI offers significant advantages, it also poses certain risks and challenges in the banking industry. One concern is the potential for biased algorithms, which could lead to unfair treatment of customers or perpetuate existing social inequalities, especially with lending operations. Additionally, there are concerns about data privacy and security, as AI relies heavily on vast amounts of customer data. Proper governance and compliance measures are essential to address these challenges and ensure responsible AI usage in the banking sector.By outlining your project objectives for us, you give us a head start in planning the best approach and estimating the actual work to be done. With a clear understanding of your vision, well-defined requirements, and a target audience to work with, we will be able to get back to you with a plan, estimates, and first drafts in mere 2-5 days from the moment we received your request.