Maven: How Artificial Intelligence is Affecting Banking & Finance

One of the best and most promising innovations in FinTech is artificial intelligence (AI). The focus of AI is to achieve better efficiency with lower costs by performing human tasks and simulating human behavior at a much higher speed. Convergint Asia Pacific, in partnership with Hanwha Vision, offers tailored, effective, and innovative AI-powered surveillance solutions that address the challenges encountered by financial institutions.

• McKinsey, aglobal consulting firm, estimates that AI could deliver up to $1 trillion in value to global banks annually.
• To remain competitive in the future market and respond to changes, the financial industry must actively adopt AI technologies and focus on developing its own technologies.
• It facilitates real-time detection and mitigation of threats through machine learning algorithms, providing immediate responses to potential breaches.
• Considering these recommendations can position financial services providers and fintechs at the forefront of this revolution, where they can drive innovation while upholding trust, transparency, and ethical standards.
• Generative AI’s ability to analyze large datasets, recognize patterns, and make informed decisions renders it invaluable in these applications.
• Half of firms say it takes over four months to ramp-up new hires, with only 17% able to do so in a month.

Using HE to encrypt the model, the bank can safely evaluate that encrypted model across multiple jurisdictions to improve outcomes. Despite such needs, few current efforts focus on protecting data while it is being used or processed, enabling financial organizations to leverage customer intelligence and other sensitive data across jurisdictions or between silos. The implementation of AI banking solutions requires continuous monitoring and calibration. Banks must design a review cycle to monitor and evaluate the AI model’s functioning comprehensively.

Artificial Intelligence (AI) in Finance

AI in finance started out as highly theoretical research, but in recent years has made huge strides toward becoming an integral part of many financial institutions. Given the success of artificial intelligence over the past few decades, it should not come as a surprise that banks are attempting to integrate artificial intelligence into every aspect of their businesses. PayPal is a good example, improving the detection of fraudulent transactions using Intel® technologies integrated into a real-time data platform from Aerospike. Key results included a 30x reduction in the number of missed fraud transactions with a 3x reduction in hardware cost.

It utilizes machine learning algorithms and natural language processing to function as a customer representative does. DataRobot provides machine learning software for data scientists, business analysts, software engineers, executives and IT professionals. Alternative lending firms use DataRobot’s software to make more accurate underwriting decisions by predicting which customers have a higher likelihood of default.

Data Collection and Analysis

By using AI algorithms, financial analysts can process and analyze data at an unprecedented scale and speed. This allows them to uncover hidden insights and trends that may have otherwise gone unnoticed. For example, AI algorithms can identify correlations between different economic indicators and predict how changes in one variable may impact others. Digital banks and loan-issuing apps use machine learning algorithms to use alternative data (e.g., smartphone data) to evaluate loan eligibility and provide personalized options. The majority of AI risk discussion has been about privacy and the inadvertent revelation of data, such as the recent FTC investigation of OpenAI.

Within just two months, ChatGPT garnered an astonishing 100 million users worldwide, with a pace of adoption that was never observed with other innovations. However, this watershed moment signifies more than a single tool like ChatGPT and the related record-breaking statistics. In fact, GenAI represents an innovation catalyst with the power to reshape finance, from risk management to cybersecurity and customer engagement.

The Impact of AI on the Finance Industry

The algorithms examine spending behaviors, transaction histories, and more to predict the enhanced creditworthiness of a customer. ZestFinance utilizes AI and ML in credit scoring by considering more unconventional data in their assessments for better risk prediction. With the use of innovative security measures like biometric authentication and risk-based authentication, AI further enhances the security measures of banks.

• Financial organizations enhance efficiency and cut costs by automating repetitive work, freeing up human resources for more strategic endeavors, and streamlining operations.
• By utilizing the power of AI in customer service departments, businesses help cut operational costs while ensuring a much more personalized experience for valued customers.
• By implementing RPA, financial institutions can improve efficiency, reduce errors, and free up employees to focus on more complex and strategic activities.
• Additionally, AI-driven behavioral biometrics examine how people use their devices, and by spotting odd patterns of behavior, they can assist in the detection of fraud.

Artificial Intelligence automate compliance operations by tracking and analyzing massive amounts of data to detect potential regulatory infractions, highlight questionable activities, and guarantee compliance standards are followed. Manual compliance checks take less time and effort, and financial institutions fulfill their regulatory requirements better as a result. The importance of Personalized financial services lies in their ability to deliver more relevant and tailored offerings to customers which increases customer pleasure, engagement, and loyalty. Financial companies better satisfy the specific demands of each consumer and enhance their overall financial well-being by comprehending their wants and offering individualized solutions. Brokerages and financial organizations utilize SOR, an algorithmic trading approach, to automatically route orders to the most advantageous exchanges or venues. AI algorithms assess market circumstances, liquidity, and order book data to choose the appropriate execution venue for trade.

AI Improves Security Orchestration, Automation and Response

AI technologies can analyze vast amounts of transactional data, identify suspicious activities, and flag potential money laundering attempts. Artificial Intelligence (AI) has transformed various aspects of our lives, including personal finance management. It has empowered individuals to take control of their finances through AI-driven financial planning and automated investment solutions. For financial services firms with operations in the EU, the EU AI Act will be effective from Spring 2024 and will govern the development, deployment and oversight of AI technologies. In short, we are seeing broad use cases for AI technologies, and the implementation of those technologies is now reaching an advanced stage for many financial service providers. Moreover, the complexity of these technologies is causing many financial services firms to rely on third-party providers to support the implementation of these applications.

In one report, 72% of financial services companies surveyed said they were adopting AI to increase revenue. This is because with increased efficiency, financial institutions can reduce costs and increase profits. Software for processing documents that combines machine learning and human verification is available from Ocrolus. People analyze financial documents more quickly and accurately by using software, businesses, and organizations.

Its Sensa AML and fraud detection software runs continuous integration and deployment and analyzes its own as well as third-party data to identify and weed out false positives and detect new fraud activity. As capabilities mature and use cases evolve, we can imagine these pros only getting better, and new opportunities emerging. AI technologies, such as machine learning and natural language processing, have revolutionized the fight against money laundering. Investment management is another area witnessing the profound impact of artificial intelligence. Advanced algorithms analyze vast financial data, including historical prices, news articles, and social media sentiment, to identify investment opportunities and optimize portfolio management. Artificial intelligence empowers investment managers to make data-driven decisions with the potential for higher returns.

HFT companies examine market data and carry out trades at breakneck rates using AI algorithms. The algorithms are made to find and take advantage of slight price disparities or market inefficiencies to make money through quick trading. Regulatory compliance is the use of AI algorithms and technologies to monitor, identify, and resolve potential breaches or non-compliant activities. Regulatory compliance works in accordance with regulatory norms and guidelines in the context of artificial intelligence for finance.

We live in the era of rapid technological progress, with the virtues of that progress finding application in a variety of industries and niches, including finance. Financial institutions have always been at the forefront of technological innovation as they deal with large masses of customer data, financial analytics, economic forecasts, and financial planning. An f5 case study provides an overview of how one bank used its solutions to enhance security and resilience, while mitigating key cybersecurity threats. The company’s applications also helped increase automation, accelerate private clouds and secure critical data at scale while lowering TCO and futureproofing its application infrastructure. Here are a few examples of companies providing AI-based cybersecurity solutions for major financial institutions.

In fact, 62% of customers would switch FSIs if they felt treated like a number, not a person. The report also found that proactive communication is rare with only 42% in banking, 49% for insurance, and 63% for wealth management. An increase in remote and hybrid work has made it more challenging for security professionals to secure widely dispersed financial data and applications.

Read more about Secure AI for Finance Organizations here.