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Leveraging AI in Banking – A Guide
Leveraging AI in Banking – A Guide

With the increased emphasis on AI in Banking, banking institutions are able to extract meaningful and actionable insights from raw data and leverage them to provide better services. Also, it benefits banking institutions by increasing workforce productivity, reducing operational costs by automating processes, reinforcing security, ensuring better compliance, and enabling exceptional user experiences all leading to an increase in top-line.

Specifically, AI solutions are enabling Digital-first approach in driving value through innovative solutions and further advancing Digital transformation in the Banking industry. There are several use cases of AI in banking, which we will be discussing in this article.

Facts and Figures

The global market for AI in Banking and FinTech Industry was estimated at USD 6.67 billion in 2019 and is expected to reach USD 22.6 billion by 2025. The market is also expected to witness a CAGR of 23.37% over the forecast period (2020-2025). Businesswire

AI in Banking - Creating Difference

Let’s take a closer look at the several areas where AI in Banking is making a difference:

  1. Financial Advisory –

    The financial industry is subject to various regulations, especially when it comes to Advisory. Decades back you had to wait in a long queue to get in touch with customer support officer to know more on your investment plans, or spend weeks bogged down by your insurance company’s bureaucracy just to get a refund after a minor car accident. AI technologies have simplified this by enabling informed decision about investments and predict possible risks using data analytics, deep learning, and machine learning algorithms.

    Today customers can easily track their finances, receive personalized financial advice, analyze their spending patterns, identify saving opportunities and manage their finances. A rapidly growing trend in AI powered financial advisory is the use of robo-advisors. Robo-advisors are AI driven virtual financial advisors that offer advice on financial matters and investment management based on logic-based queries, rules and algorithms. It is a partially automated and data-driven approach to wealth management systems, as it is done with minimal human intervention.

    As a cost-effective alternative to traditional advisors, robo-advisors have the potential to increase the reach of financial counselling to help people take better decisions related to their financial matters.

    A leading robo-advisor is Betterment, which gives its users the opportunity to buy fractional shares, and also offers automated rebalancing, tax-loss harvesting, customized retirement plans, transparent pricing, and socially responsible funds.

AI in Banking – A Sneak Peek

AI-Powered Payments

Artificial intelligence hasn’t gained a strong foothold in the payments aspect of the banking and financial industry yet. However, as it is experiencing a steady growth, it can be expected to become more prevalent in the future.

Combining artificial intelligence with payment-oriented technologies like digital wallets, digital cash and geofencing can pave the path of entirely frictionless and checkout-free experiences.

AI in banking can facilitate straight-through processing of payments, by offering better decision-making support, automating payment process workflows, and applying image recognition to documents.

  1. Fraud Detection –

    With the help of AI-powered solutions and machine learning algorithms, banks can detect transactional frauds with accuracy and speed. AI-based algorithms look for connections between applications for credit cards and loan, as well as monitor newly opened accounts to stop financial damage before it occurs. These solutions can analyze customer behavior via millions of data points to identify fraudulent activities. As a result, not only accurate fraud detections increase, but the number of false rejections reduces as well. 

    With the help of AI driven biometric-based techniques, identity verification can be made stronger, and false-positives can be avoided by extracting insights from second party and third-party data. Credit card frauds are also prevented in real-time by using classification algorithms powered by machine learning to label fraud events. This is done by leveraging AI driven algorithm training, validation and back-testing based on vast credit card transaction datasets.

    A great example of banks leveraging AI-based fraud detection products is ABANCA (a leading Spanish bank). ABANCA’s main concerns were rapidly increasing fraudulent activities from digital channels and increasing credential thefts due to more end-user actions and sophisticated attacks. The bank managed to mitigate fraud losses, cut IT expenses, and increase customer satisfaction with the help of its multi-cloud data platform and complex AI & ML algorithms. 

  2. Credit service and Loan decisions –

    Loan lending is a time-consuming process. Traditional methods require bankers to verify transaction histories and credit scores, which can end up taking a lot of time. Also, the entire process is prone to human errors, making it inaccurate and troublesome. This is another area where application of AI in Banking can enhance your customer experience.

    With the help of AI driven credit decision systems, customers’ transaction data and credit scores are analyzed quickly to determine a candidate’s eligibility for a loan. AI-powered tools are used to pick the right candidates for loans and quickly analyze candidates’ historical banking data. Hence, banking institutions are able to offer better and faster lending services to their customers.

    Lenddo is a leading company offering an AI-based solution to detect credit worthiness for improving lending services of banking institutions. Its solution uses artificial intelligence to evaluate a potential customer’s entire digital footprint and then uses its machine learning algorithm to turn it into a credit score that can help banking institutions and lenders to take a well-informed decision.

    To automate loan processes, Upstart is a leading AI-based solution. Acting as a hybrid lender, Upstart directly offers loans and uses its machine learning algorithm to make loans available for younger adults who don’t have a lot of credit history. It evaluates their years of credit, FICO scores, educational background, GPA, SAT scores, field of study, and job history to predict their creditworthiness and grant them loans accordingly.

  3. Meeting Regulatory Compliance –

    The banking industry witnesses constant changes in regulatory compliance rules due to the sensitive nature of data it deals with. Hence, it is important for banking institutions to protect their customers’ data in compliance with regulatory requirements. The inability to do so can cost them a lot, in terms of fines, loss of customers, and reputational damage.

    With the use of AI tools, Natural Language Processing (NLP), and AI-powered Regulatory Technology (RegTech), banking institutions leverage trained algorithms for customer data protection.

    A leading example is Hummingbird, which is an AI-driven tool that can be used by compliance teams for SAR validation and filing, AML investigations and reporting, compliance testing, enhancement of bank & fintech collaborations, and disputing claims and chargebacks.

  4. Financial Forecasting –

    In Investment Banking, underwriting is a time-consuming process. Underwriters perform the critical operation of appraising the credit worthiness of a potential customer and whether or not to offer a loan.

    The requirements of manual monitoring, creating risk reports, evaluating information against standards and approving or rejecting it takes up significant chunks of time, resulting in delays, reduced productivity and increased chances of errors.

    By leveraging tools powered by artificial intelligence and machine learning, investment banks are able to automate and speed up the procedures related to document management and information processing. AI technology helps in updating insights instantaneously as new data becomes available and optimize risk management strategies to create more accurate recommendations. As a result, with automation and analytical capabilities better decisions can be made and enhanced customer experiences can be achieved. 

    Manulife is a leading example of an underwriting solution that leverages artificial intelligence to modernize the underwriting process. Its intelligent underwriting model helps to predict certain medical procedures as well, such as the smoker status, thus saving a lot of time and money.

A few More Applications of AI in Banking and Fintech World

Apart from the several AI-driven solutions we have discussed earlier in this article, here are a few leading applications of AI in banking and FinTech world. These can serve as an inspiration while implementing AI in banking.

ZestAI – Formerly known as ZestFinance, ZestAI is a good example of AI in Banking. It is leading player in the FinTech world as it offers a high-quality, AI-driven credit underwriting solution. Its solution offers a custom machine learning scoring model, delivers live scoring and reason codes within your loan origination system, takes care of compliance documentation, offers multivariate monitoring & business intelligence, and gives professional services for smooth adoption and ongoing support. – It is a highly efficient AI & ML driven tool that analyzes multiple data points from credit bureau sources to model credit risks accurately for consumers. Banking institutions can improve their lending performance as uses artificial intelligence and machine learning techniques based on statistics on your portfolio leading to quicker informed decision making.

DataRobot – DataRobot is an AI-driven cloud platform that helps banking institutions to accelerate their GTM (go to market). With its low-risk model and API-based deployment capabilities, banking institutions can get their ideas to production faster. Also, it can help to convert massive amounts of data into actionable insights and metrics so that profit margins can be increased. Its ML algorithms create a predictive analytics model than helps banking institutions to take informed decisions about fraudulent attacks and increase their security.

Enlighten – Developed by Kasisto, Enlighten is an intelligent digital assistant solution that enables financial institutions to create engaging digital customer experiences. It combines financial data, behavioral data, and interaction history with artificial intelligence to provide intuitive, intelligent, and proactively insightful hyper-personalized experiences - a very effective usa of AI in Banking. 

RPA and Chatbots

RPA enables banking institutions to automate manual business process. As a result, banking institutions are able to enhance their business productivity, reduce operational costs and scale up their services. An additional AI layer over the traditional RPA is helping banks to provide enhanced experiences to the customers.

AI-based chatbots have multiple use cases of their own in the banking industry:

  • Smart Sales Processes: Chatbots are highly efficient tools that carry out several sales activities seamlessly.
  • Enhanced In-App Experiences: In-app tasks like opening a bank account or paying bills can be supported by an AI assistant.
  • Advice from Transaction Data: With AI, complex transaction data gets converted into actionable information, so that customers can leverage it to carry out their tasks like budgeting efficiently.

Way Forward for AI in Banking

To modernize banks, their frameworks and their processes with out interrupting the core business is one of the significant difficulties which banking industry faces. AI in Banking is not here to replace physical banker but for optimizing how banks work by helping them become more agile, make smarter decisions, and stay more competitive.

With detailed planning and thoughtful implementation, artificial intelligence can have an exceptional impact on the banking and industry with innumerable applications in real world. However, banks need to pull together people, processes, and data, to work in a cohesive and collaborative fashion.

Are you, also, looking to get the benefits of digital transformation by leveraging AI in Banking? Connect with our experts, and we will help you integrate digital technologies in building your financial solution. Qentelli’s experts have in-depth domain knowledge to provide top-quality banking solutions. Drop us an email