AI: RESHAPING BANKING
The emergence of artificial intelligence (AI) has revolutionized the banking industry, paving the way for smart banking and opening doors to a smarter future. By harnessing the power of AI, financial institutions can deliver personalized, efficient, and secure services to customers, ultimately empowering individuals and businesses to make more informed financial decisions.
Banks must realize that fully integrated AI is a journey — a series of steps and gradual competencies to work up to over the next several years. One of those steps is the gradual improvement of messy internal processes so that entire teams or divisions take steps toward using data and AI to work smartly, efficiently, and within regulatory standards.
AI intimidates many banks even though they’ve already laid the foundations for successful AI projects for years or decades. Quants, algorithmic traders, risk analysts, fraud analysts, pricing teams, the list goes on — these people and teams already form the building blocks of a robust, inclusive strategy. Successful banks build upon this pre-existing framework.
With the banking and financial sector accounting for approximately 20-25% of the global economy, it becomes evident that AI holds immense potential in terms of driving business benefits. In fact, the utilization of AI in the banking sector has the potential to generate colossal revenue, estimated to exceed $1 trillion in the coming years.
The end goal (Everyday AI) for banks means turning data from the cost center it is today into a source of efficiency and a wealth of information that provides fundamental value to the business.
Banks encounter four major challenges while implementing AI:
Cost of Technology and Data Essentials: One of the biggest roadblocks to successfully scaling AI in banking is not putting ML models into production or creating them themselves. Fundamental data management enables an organization to leverage data from the bottom up, democratizing data use across teams and roles.
This challenge is not unique to banking — a cross-industry survey revealed that no matter what the business, fundamentals like cleaning and wrangling data and connecting to data sources consistently rank as the top challenge for participants. Many banks consider building their own platforms to customize, control and secure less costly models or avoid vendor lock-in. However, the question is whether such a practice will enable continuous innovation or lessen maintenance effort and cost.
The Regulatory Environment: When it comes to AI in banking is, of course, the regulatory environment. Between the European Union’s General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), the New York Department of Financial Services Cybersecurity Regulation, third-party risk management (TPRM) expectations, and the SEC disclosure guidance — the trends in banking compliance regulations when it comes to data are complex and increasing. The outlook becomes complex and nuanced once combined with additional regulations surrounding financial crime, Financial Accounting Standards Board (FASB) standards, and more.
More so than any other industry, banks must put concerns of transparency and reproducibility at the forefront. In fact, this challenge alone carries such heavy consequences that banks often shy away from scaling AI efforts.
Banks will need to build inherently interpretable models to comply with regulatory requirements. Yet it is essential to acknowledge that what can make ML models remarkably accurate is often also what makes their predictions difficult to understand: they are very complex. Building effective and transparent models with the right tools and approach is possible.
In addition to building models that can be interpreted from the start, there is the governance issue itself. Data and analytics teams in banks are often paralyzed by uncertainty due to a proliferation of data privacy laws.
Model Risk Management and Validation: The struggle between velocity and proper processes for model risk validation can leave an AI project moribund. Mitigating risk is the top priority, meaning that model risk review must be sped up to uphold the validation quality.
However, many improvements can be made and fundamentally better ways to address this challenge, namely by introducing consistency and reproducibility into the process of model sign-off before production and by bringing this process as close to the reality of the built models as possible.
MRM teams may look at models from different organizations or groups across the company, each with unique processes, and may construct models in different systems with different techniques and capabilities. As a result, for each review, the model risk validation team needs more time to get their bearings. Similarly, with a consistent system or process by which models are delivered, the next step (deployment to production) is smooth. Creating consistent and automated model risk reporting and an integrated production approval process is essential to speed up the process.
Hiring: Creating collaborative teams with technology competency is an important element of AI strategy. Yet, many things still need to be clarified about the hiring process. For banks, hiring is even more of a challenge because it can be difficult to find people who are cutting-edge in their AI technology knowledge and have a deep understanding of the industry.
Banks must realize that fully integrated AI is a journey — a series of steps and gradual competencies to work up to over the next several years. One of those steps is the gradual improvement of messy internal processes so that entire teams or divisions take steps toward using data and AI to work smartly, efficiently, and within regulatory standards.
AI intimidates many banks even though they’ve already laid the foundations for successful AI projects for years or decades. Quants, algorithmic traders, risk analysts, fraud analysts, pricing teams, the list goes on — these people and teams already form the building blocks of a robust, inclusive strategy. Successful banks build upon this pre-existing framework.
With the banking and financial sector accounting for approximately 20-25% of the global economy, it becomes evident that AI holds immense potential in terms of driving business benefits. In fact, the utilization of AI in the banking sector has the potential to generate colossal revenue, estimated to exceed $1 trillion in the coming years.
The end goal (Everyday AI) for banks means turning data from the cost center it is today into a source of efficiency and a wealth of information that provides fundamental value to the business.
Banks encounter four major challenges while implementing AI:
Cost of Technology and Data Essentials: One of the biggest roadblocks to successfully scaling AI in banking is not putting ML models into production or creating them themselves. Fundamental data management enables an organization to leverage data from the bottom up, democratizing data use across teams and roles.
This challenge is not unique to banking — a cross-industry survey revealed that no matter what the business, fundamentals like cleaning and wrangling data and connecting to data sources consistently rank as the top challenge for participants. Many banks consider building their own platforms to customize, control and secure less costly models or avoid vendor lock-in. However, the question is whether such a practice will enable continuous innovation or lessen maintenance effort and cost.
The Regulatory Environment: When it comes to AI in banking is, of course, the regulatory environment. Between the European Union’s General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), the New York Department of Financial Services Cybersecurity Regulation, third-party risk management (TPRM) expectations, and the SEC disclosure guidance — the trends in banking compliance regulations when it comes to data are complex and increasing. The outlook becomes complex and nuanced once combined with additional regulations surrounding financial crime, Financial Accounting Standards Board (FASB) standards, and more.
More so than any other industry, banks must put concerns of transparency and reproducibility at the forefront. In fact, this challenge alone carries such heavy consequences that banks often shy away from scaling AI efforts.
Banks will need to build inherently interpretable models to comply with regulatory requirements. Yet it is essential to acknowledge that what can make ML models remarkably accurate is often also what makes their predictions difficult to understand: they are very complex. Building effective and transparent models with the right tools and approach is possible.
In addition to building models that can be interpreted from the start, there is the governance issue itself. Data and analytics teams in banks are often paralyzed by uncertainty due to a proliferation of data privacy laws.
Model Risk Management and Validation: The struggle between velocity and proper processes for model risk validation can leave an AI project moribund. Mitigating risk is the top priority, meaning that model risk review must be sped up to uphold the validation quality.
However, many improvements can be made and fundamentally better ways to address this challenge, namely by introducing consistency and reproducibility into the process of model sign-off before production and by bringing this process as close to the reality of the built models as possible.
MRM teams may look at models from different organizations or groups across the company, each with unique processes, and may construct models in different systems with different techniques and capabilities. As a result, for each review, the model risk validation team needs more time to get their bearings. Similarly, with a consistent system or process by which models are delivered, the next step (deployment to production) is smooth. Creating consistent and automated model risk reporting and an integrated production approval process is essential to speed up the process.
Hiring: Creating collaborative teams with technology competency is an important element of AI strategy. Yet, many things still need to be clarified about the hiring process. For banks, hiring is even more of a challenge because it can be difficult to find people who are cutting-edge in their AI technology knowledge and have a deep understanding of the industry.
STREAMLINE OPERATIONS & EFFICIENCY
According to a recent survey, over 75% of banks have already implemented AI solutions in their operations. This demonstrates the significant adoption of AI technology within the industry.
AI-powered automation is transforming the way banks handle repetitive and time-consuming tasks. Manual data entry, document processing, and customer inquiries can now be automated using AI algorithms and chatbots. By automating these mundane tasks, banks can reduce errors, minimize processing time, and allocate human resources to focus on more complex and value-added activities.
Studies show that 80% of customers find AI-powered chatbots helpful in resolving their queries quickly and efficiently. As AI continues to evolve, the potential for streamlining operations and enhancing efficiency in banking becomes even more promising.
Liveplex API technology helps integrate AI with other technologies, such as Robotic Process Automation (RPA) and blockchain, which can further optimize operations and foster seamless collaboration across various departments.
ENHANCING CUSTOMER EXPERIENCES
One of the most striking benefits of AI in banking is its impact on customer experiences. By analyzing extensive customer data, AI algorithms gain deep insights into individual preferences, spending habits, and financial goals. This enables banks to deliver highly personalized services and recommendations.
In fact, studies have shown that nearly 70% of customers appreciate and value personalized services offered by AI-powered banking systems. These tailored experiences not only increase customer satisfaction but also foster a sense of financial empowerment and trust in the banking institution.
AI algorithms analyze vast amounts of customer data, including transaction history, spending patterns, and browsing behavior, to gain deep insights into everyone’s financial needs. This allows banks to understand their customers better and tailor their offerings accordingly. From personalized product recommendations to customized financial advice, AI empowers banks to provide highly relevant solutions to their customers.
Imagine customers logging into the banking app and receiving personalized insights and recommendations based on their spending habits and financial goals. AI algorithms can analyze transaction histories, identify potential savings opportunities, or suggest investment options aligned with their risk appetite.
Moreover, AI-powered chatbots and virtual assistants transform how customers interact with banks. These intelligent systems can handle various inquiries, providing instant and accurate responses. Whether checking account balances, initiating transactions, or seeking financial advice, AI-powered assistants are available 24/7, delivering efficient and personalized support.
Furthermore, AI enables banks to leverage customer data to offer targeted and timely promotions. Banks can deliver personalized offers and rewards by analyzing customer behavior and preferences, increasing customer engagement and loyalty. For example, receiving tailored credit card offers or exclusive discounts on products or services that align with customers’ interests can enhance their overall banking experience. Liveplex can develop custom Intelligence for Agents and Brand Ambassadors of the Bank's virtual presence to enhance customer experience.
FRAUD DETECTION AND RISK MANAGEMENT
Financial institutions face ever-evolving threats from fraudsters and cybercriminals. However, AI is proving to be a game-changer in combating fraudulent activities and managing risks effectively. Machine learning algorithms can analyze historical transaction data, detect suspicious patterns, and flag potentially fraudulent activities in real-time. This proactive approach enables banks to take swift action, preventing fraudulent transactions and safeguarding their customers' financial assets.
Did you know that AI-powered fraud detection systems have shown an impressive success rate of over 95% in accurately identifying fraudulent activities? This statistic highlights AI's significant impact on fortifying financial institutions' security measures.
AI-driven systems continuously learn and adapt to new fraud techniques, staying one step ahead of fraudsters. AI algorithms can identify emerging patterns and anomalies that may indicate fraudulent activities by analyzing large volumes of data, including transactional data, customer behavior, and industry-wide trends. This dynamic approach allows banks to stay proactive in their fraud prevention strategies.
In addition to preventing fraudulent activities, AI plays a crucial role in managing the risks associated with financial operations. With their ability to analyze complex data sets and identify patterns, AI algorithms can assess and mitigate various types of risks, such as market, credit, and operational risks. By leveraging AI-powered risk management tools, banks can make informed decisions, minimize potential losses, and ensure the stability of their operations.
According to a recent study, banks implementing AI-powered risk management systems have seen a 65% reduction in risk-related losses. This statistic demonstrates the tangible impact of AI on risk mitigation within the financial industry.
As AI continues to evolve, we can expect even more exciting advancements in the banking industry. From improving customer experiences and operational efficiency to enhancing fraud detection and risk management, AI is reshaping the landscape of financial services.
Are you ready to take your banking and financial services to the next level with AI? Liveplex has your back! Talk to us at hello@liveplex.io
According to a recent survey, over 75% of banks have already implemented AI solutions in their operations. This demonstrates the significant adoption of AI technology within the industry.
AI-powered automation is transforming the way banks handle repetitive and time-consuming tasks. Manual data entry, document processing, and customer inquiries can now be automated using AI algorithms and chatbots. By automating these mundane tasks, banks can reduce errors, minimize processing time, and allocate human resources to focus on more complex and value-added activities.
Studies show that 80% of customers find AI-powered chatbots helpful in resolving their queries quickly and efficiently. As AI continues to evolve, the potential for streamlining operations and enhancing efficiency in banking becomes even more promising.
Liveplex API technology helps integrate AI with other technologies, such as Robotic Process Automation (RPA) and blockchain, which can further optimize operations and foster seamless collaboration across various departments.
ENHANCING CUSTOMER EXPERIENCES
One of the most striking benefits of AI in banking is its impact on customer experiences. By analyzing extensive customer data, AI algorithms gain deep insights into individual preferences, spending habits, and financial goals. This enables banks to deliver highly personalized services and recommendations.
In fact, studies have shown that nearly 70% of customers appreciate and value personalized services offered by AI-powered banking systems. These tailored experiences not only increase customer satisfaction but also foster a sense of financial empowerment and trust in the banking institution.
AI algorithms analyze vast amounts of customer data, including transaction history, spending patterns, and browsing behavior, to gain deep insights into everyone’s financial needs. This allows banks to understand their customers better and tailor their offerings accordingly. From personalized product recommendations to customized financial advice, AI empowers banks to provide highly relevant solutions to their customers.
Imagine customers logging into the banking app and receiving personalized insights and recommendations based on their spending habits and financial goals. AI algorithms can analyze transaction histories, identify potential savings opportunities, or suggest investment options aligned with their risk appetite.
Moreover, AI-powered chatbots and virtual assistants transform how customers interact with banks. These intelligent systems can handle various inquiries, providing instant and accurate responses. Whether checking account balances, initiating transactions, or seeking financial advice, AI-powered assistants are available 24/7, delivering efficient and personalized support.
Furthermore, AI enables banks to leverage customer data to offer targeted and timely promotions. Banks can deliver personalized offers and rewards by analyzing customer behavior and preferences, increasing customer engagement and loyalty. For example, receiving tailored credit card offers or exclusive discounts on products or services that align with customers’ interests can enhance their overall banking experience. Liveplex can develop custom Intelligence for Agents and Brand Ambassadors of the Bank's virtual presence to enhance customer experience.
FRAUD DETECTION AND RISK MANAGEMENT
Financial institutions face ever-evolving threats from fraudsters and cybercriminals. However, AI is proving to be a game-changer in combating fraudulent activities and managing risks effectively. Machine learning algorithms can analyze historical transaction data, detect suspicious patterns, and flag potentially fraudulent activities in real-time. This proactive approach enables banks to take swift action, preventing fraudulent transactions and safeguarding their customers' financial assets.
Did you know that AI-powered fraud detection systems have shown an impressive success rate of over 95% in accurately identifying fraudulent activities? This statistic highlights AI's significant impact on fortifying financial institutions' security measures.
AI-driven systems continuously learn and adapt to new fraud techniques, staying one step ahead of fraudsters. AI algorithms can identify emerging patterns and anomalies that may indicate fraudulent activities by analyzing large volumes of data, including transactional data, customer behavior, and industry-wide trends. This dynamic approach allows banks to stay proactive in their fraud prevention strategies.
In addition to preventing fraudulent activities, AI plays a crucial role in managing the risks associated with financial operations. With their ability to analyze complex data sets and identify patterns, AI algorithms can assess and mitigate various types of risks, such as market, credit, and operational risks. By leveraging AI-powered risk management tools, banks can make informed decisions, minimize potential losses, and ensure the stability of their operations.
According to a recent study, banks implementing AI-powered risk management systems have seen a 65% reduction in risk-related losses. This statistic demonstrates the tangible impact of AI on risk mitigation within the financial industry.
As AI continues to evolve, we can expect even more exciting advancements in the banking industry. From improving customer experiences and operational efficiency to enhancing fraud detection and risk management, AI is reshaping the landscape of financial services.
Are you ready to take your banking and financial services to the next level with AI? Liveplex has your back! Talk to us at hello@liveplex.io