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AI Applications, Use Cases, and Examples in Finance

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“The article was originally published on AIMultiple

As seen in the image above, interest in artificial intelligence (AI) in finance is increasing, like in other industries. According to a 2020 Business Insider report, 75% of respondents at banks with over $100 billion in assets are implementing AI technologies. McKinsey shares that banking and other financial service companies can generate more than $250 billion in value by applying AI technologies in their financial processes.

However, there is still a long way for AI models to be widely used in financial services. For example, historical bias can be an issue in automated credit scoring. AI models could take into account variables like gender, race, or profession which may have been used historically in credit applications. Banks need to monitor models to avoid such situations.

Although the integration of AI into finance needs further development, the benefits definitely outweigh the potential costs. AI technologies will help banks and other financial institutions accelerate their processes with reduced cost and error while ensuring data security and compliance.

Lending

Retail Lending Operations

Thanks to document capture technologies, financial institutions can automate their credit applicant evaluation processes. Instead of reviewing financial documents like payslips or invoices manually, which is a tiring task, AI algorithms can handle this operation, capture data from documents automatically, and manage lending operations with less human intervention. This will enable banks and financial institutions to conclude credit applications faster and with fewer errors.

Commercial Lending Operations

Similarly, financial companies can capture relevant data from borrower companies’ financial documents, like annual reports and cash flow statements. With the extracted data, credit evaluation can be handled much accurately, and banks can provide faster services for lending operations.

Retail Credit Scoring

Financial companies can leverage AI to evaluate credit applications faster and more accurately. AI tools leverage predictive models to assess applicants’ credit scores and enable reduced compliance and regulatory costs on top of better decision-making. For example, Discover Financial Services has accelerated its credit assessment processes by ten times and achieved a more accurate view of borrowers by using AI technologies in evaluating credit applicants. For more on credit scoring, feel free to read our article on the topic or access an interactive list of leading vendors in the space.

Commercial Credit Scoring

AI can analyze relevant financial information and provide insights about financials by leveraging techniques like machine learning and natural language processing. Instead of conducting numerous calculations in spreadsheets or financial documents, AI can rapidly handle large volumes of documents and deliver insights without missing an important point. This can enable better commercial loan decisions.

Investments

Robo-Advisory

Companies can offer AI chatbots and virtual assistants to monitor personal finances. These assistants can provide insights based on target savings or spending amounts. Besides giving insights on personal finances, robo-advisors can give financial advice to help investors manage their portfolio optimally and recommend a personalized investment portfolio containing shares, bonds, and other asset types. To do that, robo-advisors use customers’ information about their investment experience and risk appetite.

Operations

Debt Collection

The Consumer Financial Protection Bureau (CFPB) shares that “Continued attempts to collect debt not owed” is the most common complaint by 39% in the US in 2017. Banks and other financial institutions can use AI to solve this issue and provide a compliant and efficient debt collection process. According to the CEO of Brighterion, a MasterCard company, effective use of AI can help reduce delinquency rates by 76%.

Procure-to-pay

Companies can introduce AI-based invoice capture technologies to automate their invoice systems and use accessible billing services that remind their customers to pay. These will enable businesses to accelerate their processes, reduce any manual errors and costs, and improve loan recovery ratios. Feel free to read our in-depth source-to-pay automation guide to learn more.

Account Reconciliation in Commercial Banking

Companies can leverage AI to extract data from bank statements and compare them in complex spreadsheets. By using AI, account reconciliation processes can be accelerated significantly, and errors that can cause significant disruption would be eliminated.

Insurance

Insurance Pricing

Like credit applications, AI can assess customers’ risk profiles and identify the optimal prices to quote with the right insurance plan. This would decrease the workflow in business operations and reduce costs while improving customer satisfaction.

Claims Processing

Claims processing includes multiple tasks, including review, investigation, adjustment, remittance, or denial. As AI can rapidly handle large volumes of documents required for these tasks thanks to document processing technologies, it can also detect fraudulent claims and check if claims fit regulations.

As an example, Tractable has introduced an AI system that can recognize accident images and estimate repair costs in real-time. As a result, it claims that insurance companies can accelerate claims processing by ten times.

Audit and Compliance

Fraud Detection

According to KPMG, the main challenge that banks face today is cyber and data breaches. More than half of the survey respondents share that they can only recover less than 25% of fraud losses, which makes fraud prevention necessary.

AI technologies advanced significantly to detect fraudulent actions and maintain system security. Using AI for fraud detection can also improve general regulatory compliance matters, lower workload, and operational costs by limiting exposure to fraudulent documents. In a case study by Vectra.ai, DZ Bank has reduced the workload of security operations teams by 36x.

Regulatory Compliance

Complying with regulatory requirements is essential for banks and other financial institutions. AI can leverage Natural Language Processing (NLP) technologies to scan legal and regulatory documents for compliance issues. As a result, it is a scalable and cost-effective solution because AI can browse thousands of documents rapidly to check non-compliant issues without any manual intervention.

Travel & Expense Management

Expenditure reports require travel receipt checks (like hotel reservations, flight tickets, gas station receipts, etc.) for compliance, VAT deduction regulations, and income tax laws. While this task includes compliance risks concerning fraud and payroll taxation, AI can leverage deep learning algorithms and document capture technologies to prevent non-compliant spending and reduce approval workflows.

Customer Service

Know Your Customers (KYC) Processes

By leveraging AI technologies like natural language processing and data extraction models, banks can find anomalous patterns and identify areas of risk in their KYC processes without human intervention. For edge cases where human interaction is needed, the case can be forwarded for approval. The integration of AI technologies will have benefits like accelerated processing times, improved security and compliance, and reduced errors in these processes.

Responding to Customer Requests

Conversational AI systems can instantly support customers to fulfill their requests. In cases where the claim is not resolved, humans can intervene. By integrating AI into customer service, customer requests are addressed faster, the workload of call center workers would be reduced, and they can focus on more complex customer requests.

Identification of upsell & cross-sell opportunities

Banks and other financial institutions can accurately discover unaddressed customer needs, thanks to CRM systems and AI technologies. This can help increase customer satisfaction while increasing revenues for the financial institution. For example, a company can offer car insurance to its customer who is in the process of buying a car. Oliver Wyman shares that using AI insights can increase annual income from email cross-sell by four times.

Customer Churn Prediction

AI models can detect patterns in customer behaviors and predict which customers have a higher potential to churn in the next term. By analyzing these behaviors, banks and other financial institutions can identify why a customer is at risk and take actions accordingly to prevent churn.

Other

Trading

Investment companies have started to use AI to detect patterns in the market and predict their future values. By that, AI can discover a broader range of trading opportunities where humans can’t detect. Another benefit of AI is that it can analyze large amounts of complex data faster than people, which provides time and money-saving. Kavout, an AI trading service, estimates that they can approximately generate 4.84% with their AI-powered trading models.

However, algorithmic trading still has a way to be used more widely as it is still unable to perform better than humans. According to Bloomberg, the share of hedge funds that use AI decreased by 7.3% in March 2018. It has fallen by 2.4% in the previous period.

 

Cem Dilmegani, Founder, AIMultiple

 

Cem is a high-tech industry analyst and he served as a tech consultant at McKinsey and as a tech entrepreneur at Hypatos, the document hyperautomation company. AIMultiple.com provides 1M enterprises with transparent, data-driven insights on enterprise technology.

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Features

HOW FSI INCUMBENTS CAN STAY RELEVANT THROUGH THE GCC’S PAYMENTS EVOLUTION

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payment

By Luka Celic, Head of Payments Architecture – MENA, Endava

Banks and payment services providers (PSPs) have been the region’s engines of economic growth for as long as anyone can remember. It is therefore jarring to imagine that this dominance is now under threat. After all, venerable banks and credit card companies have elegantly embraced the Internet, mobile banking, and the cloud to deliver self service banking to millions of customers. But consumers, especially digital natives, have never been known for congratulating an industry for a job well done. Instead, with each convenience, their expectations only grow. The siege reality of the pandemic accelerated a shift in consumer behaviour, and Middle East banks and PSPs now face challenges on three fronts.

The first is FinTechs. from Saudi Arabia’s BNPL (buy now, pay later) pioneer Tamara and Qatar’s unbanked oriented platform cwallet, to online financial services, Klarna, tech startups have been able to tap into rapidly changing consumer markets. New companies find it easier to pivot. And like speed boats racing against aircraft carriers, they weaved effortlessly to fulfil a range of desires amid high smartphone connectivity rates and a range of other favourable market conditions. By one estimate from 2022, BNPL alone accounted for US$1.5 billion (or 4%) of the Middle East and Africa’s online retail market.

The second threat is open banking, which comes in many forms, but one example is the instant-payments platforms being introduced by central banks such as those in Saudi Arabia and the United Arab Emirates. To get a sense of how this could play out, we need only look to Europe, where players who once relied on payments through card schemes are now pivoting towards open banking enabled payments. Closer to home, Al Ansari Exchange recently announced its customers can now transfer money and settle bills via the recipient’s mobile number, enabled by the UAE’s Aani IPP.

And finally, comes big tech. To augment its e-wallet service, Apple has signed up to an open banking service in the UK. The open banking framework which banks enabled through their investments is being exploited by a Big Tech firm that has access to 34% of UK smartphone users. Unsurprisingly, this sparked a fierce antitrust complaint by UK’s banks. Other big names will surely follow as they continue to craft ways of offering the digital experiences that garnered them user loyalty in the first place.

THE BALANCE

Apple Wallet is aimed at blending payment methods, loyalty cards, and other services into a single experience. But such moves have raised regulators’ eyebrows regarding a lack of interoperability and the preservation of competitive markets. Hence, Apple’s open banking foray — a gesture to calm the nerves of a finance market that fears having to compete with a company armed with countless millions of user transactions from which to draw insights. The massive user bases of tech giants will give any FSI CEO goosebumps. How does a traditional bank lure an Apple user? Open banking initiatives open the door to greater competition and innovation, both of which are good for consumers. But the only way to ensure both is by building an ecosystem that balances innovation with regulatory oversight.

FROM INCUMBENT TO INNOVATOR

Yes, smaller businesses have freedom of movement that larger incumbents do not. But that does not mean that there are no paths for banks and PSPs. There are, in fact, several strategies that larger FSI companies can employ to capitalise on the open banking revolution.

The first of these is collaborating to create ecosystems that provide users with frictionless experiences. Established FSIs already have access to a wealth of information about their customers and must now consider how to integrate data sources to create highly streamlined and frictionless workflows. A customer applying for a loan could then see their details auto populated, and credit history already accounted — all without the hassle of lengthy phone calls, application forms, or submission requests. In an age when instant is everything, it’s easy to see why the former approach could foster loyalty, while the latter would only serve to drive customers towards more capable competitors.

Card companies and issuer banks could also work with acquirers to smooth out the rough landscape that has arisen from the advent of digital payments. Acquirers traditionally acted on behalf of the merchants that accepted payment methods to recoup funds from the PSP through the issuing bank. This system has served the industry well, but with more payment methods emerging, acquirers have branched out into mobile wallets, QR codes, and gateway services. Gradually the relevance of established players has dwindled as their lack of representation at the critical checkpoint has diminished their significance. Incumbents must work to turn back the tide by recognising that acceptance and acceptance ownership are becoming increasingly important for maintaining market relevance.

Another strategy is diversification. Veteran FSIs may feel like they’ve lost ground to nimble start-ups and Neo Banks, but history shows value in patience — established FSI players now benefit from the investments of early innovators, and double down on payments innovations which have already shown the most promise. Moreover, if they diversify their portfolios through acquisitions, innovations, and partnerships, they can secure their future. Mastercard presents an excellent example with their US$200m investment into MTM payments. This single move has given the company access to MTM’s 290 million strong subscriber base, allowing these customers to become familiar with Mastercard products before getting entrenched with mobile wallet alternatives.

WHO’S ON TOP?

If we look at the rise of BNPL services, we see an origin story with — at least — major supporting roles for large card providers. But open banking has sidelined them in just a few years. BlackBerry was a stock market darling just five years before it sought a buyer. Traditional FSI players must innovate; they must collaborate with emerging disruptors; they must diversify. They can survive and thrive if they do these things — after all, they already have much of the infrastructure, and experience required for success. Middle East banks and PSPs have the existing user bases, so they have the scale to get out in front in the era of open banking. All they lack is the kind of compelling use cases that will entice the banking public. PSPs and their issuers could offer embedded payments, for example. The right services at the right time will be warmly received by consumers, no matter the scale of the offering institution, so there is every reason to believe that incumbents will come out on top against FinTech and Big Tech.

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Features

SEC paves way to approve spot ethereum ETFs

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ETF

By Simon Peters, Crypto Analyst at eToro

Ethereum spot ETFs took a significant step forward to being available to US investors last week with approval of the 19b-4 applications, allowing US exchanges (namely Cboe BZX, NYSE Arca and Nasdaq) to list and trade ethereum spot ETFs.

On the back of this, ethereum has been one of the best performing cryptoassets this week, gaining 19%.

According to a recent survey by eToro with retail investors in the UAE, over 74% respondents agreed that the prospect of an ethereum ETF will significantly influence their decision to increase, decrease or maintain their current ethereum allocation.
Focus now turns to the S-1 registration statements from the ETF issuers, as these still need to be approved by the SEC before the ethereum spot ETFs can actually launch and investors can buy them.

As to when the S-1s will be approved we have to wait and see. It could be weeks or months unfortunately.

Nevertheless, with the 19b-4s out of the way, it could be an opportunity now for savvy crypto investors to buy ethereum in anticipation of the S-1s being approved, frontrunning the ETFs going live and the billions of dollars potentially flowing into these.

We’ve seen what happened when the bitcoin spot ETFs went live, with the bitcoin price going to a new all-time high in the months after. Could the same happen with ethereum? The all-time high for ethereum is $4870, set back in 2021. We’re currently at $3650, about 35% away.

We’re also going into a macroeconomic climate with potentially looser financial conditions, i.e. interest rate cuts and a slowdown of quantitative tightening, conditions where risk assets such as crypto tend to perform well price-wise.

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Harnessing AI and big data to transform Middle East’s retail industry landscape

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unifonic

By Saeed Alajou, Senior Sales Director, Enterprise Business

With the increasing dominance of technological advancements in the current era, the global retail industry is witnessing a massive shift in its operations. As the industry embraces a varied range of cutting-edge technologies such as artificial intelligence (AI) and big data analytics, it is redefining customer expectations and the conventional concepts of business operations. According to recent studies, The global artificial intelligence (AI) in retail market size is projected to grow from $9.36 billion in 2024 to $85.07 billion by 2032, at a CAGR of 31.8% from 2024 to 2032. This transformative wave is compelling companies to harness the potential of these cutting-edge technologies to maintain their competitive edge.

One of the most evident trends in this era is the convergence of eCommerce, AI and data analytics, which is driving the evolution of the retail landscape worldwide. In the current omnichannel retail landscape, consumers expect consistency and continuity across various touchpoints, pushing industry players to integrate conversational AI. This integration ensures a seamless experience; for example, customers can begin a conversation with a chatbot while browsing online and effortlessly continue it via a mobile app when they visit a physical store.

However, the potential of the omnichannel approach and conversational AI platforms is not limited to supporting customers. They also provide retailers with valuable insights into customer behaviour across different channels. Conversational AI platforms can generate a vast amount of data from customer interactions, offering retailers valuable insights into consumer preferences, trends, and pain points. By analysing this data, retailers can uncover patterns, identify emerging trends, and optimise their product offerings and marketing strategies accordingly.

Furthermore, AI-driven analytics enable retailers to gauge customer sentiment, allowing them to address issues and enhance satisfaction proactively. These data-driven insights empower retailers to make informed decisions and stay ahead of the curve. Reflecting the vast potential of AI, the retail sector in the Middle East is rapidly adopting this technology, becoming a leading industry in AI investment. Reports indicate that AI spending in the Middle East and Africa (MEA) reached USD 3 billion and is expected to grow to USD 6.4 billion by 2026, with a compound annual growth rate (CAGR) of 29.7 per cent.

The innovation of chatbots and virtual assistants has accelerated the integration of AI technologies in retail, revolutionising customer interactions by adding a human-like touch to digital engagements. These tools enhance the purchasing journey, making it more intuitive and responsive, providing customised and real-time recommendations based on consumer sentiment. However, retailers need to manage expectations of scalability and ensure AI complements rather than replaces human interactions.

Furthermore, integrating big data into retail operations helps understand customer behaviour and preferences. Retailers can leverage vast amounts of data to gain insights into customer needs and tailor their offerings accordingly. By analysing customer-generated data, businesses can conduct predictive analysis to anticipate trends and make informed decisions, keeping them ahead of the curve in offering products and services that resonate with their target audience.

When it comes to the impact of AI integration in the retail sector, one key segment where it is significantly visible is the supply chain. By integrating big data analytics, retailers are achieving more efficiency in their supply chain operations. Predictive analytics powered by AI aids in forecasting demand, optimising inventory levels, reducing waste, and ensuring products are available when and where customers need them. This enhances operational efficiency and customer satisfaction by minimising stockouts and delays.

AI integration supports a customer-centric approach in retail, and it positions technology as a key facilitator in meeting customer demand. Advanced technologies can identify and replicate demographic needs and pinpoint where investment is required to add value. The integration of various AI tools including price-matching technologies, pay-per-click advertising optimisation, and predictive analytics, aids the retailers in focusing on perfecting the customer journey, ensuring a seamless and enjoyable experience from the start to finish.

Although AI is widely embraced across the industry regardless of company size, delivering the best customer service requires empowering employees with the right tools and knowledge. When employees are equipped with AI-driven insights, they can provide more personalised and efficient service, enhancing the overall customer experience. This empowerment also promotes a culture of innovation and continuous improvement within the organization.

Additionally, data integration and integrity are crucial for the effectiveness of AI and big data. Retailers must implement systems that can integrate data from various sources, ensuring that all information is accurate, consistent, and up to date. This collaborative approach allows retailers to offer a unified brand experience across all channels while maintaining data boundaries and complying with privacy regulations.

This widespread adoption of AI technologies in the industry underscores the importance of establishing a robust and adaptable regulatory framework. Given the growing concerns about data privacy and ethical use, retailers must ensure responsible and secure handling of customer data. Stagnant regulations can lead to compliance issues and erode customer trust, and this necessitates current and customer-aligned regulations to maintain a trustworthy data environment.

Another challenge in AI integration is utilising AI and big data to experiment with new ideas and strategies. In retail, embracing calculated risks is crucial for innovation and growth, viewing risks as learning opportunities. Being responsive to evolving customer needs allows retailers to navigate uncertainties and capitalise on opportunities for success.

With AI projected to contribute up to USD 320 billion to the Middle East’s economy by 2030, the region is increasing its investment in technology. This emphasises the need for a holistic approach in retail, integrating AI, big data, and a customer-centric mindset to thrive in the market. The industry players can maintain their competitive edge by focusing on efficiency in supply chain operations, understanding consumer behaviour, and empowering employees.

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