What Generative AI Means For Banking

Businesses use predictive AI to forecast future demand levels based on past trends. This helps businesses plan resource allocation and manage inventory levels accordingly. Reach out to us for high-quality software development services, and our software experts will help you outpace you develop a relevant solution to outpace your competitors. The possibilities of generative AI in education are endless—from helping students with disabilities to inspiring new startups.

Its capability to generate unique and meaningful outputs from human language inputs has made this technology particularly invaluable for streamlined customer service, financial report generation, personalized investment advice, and more. Looking ahead, AI continues to drive innovation in banking, positioning businesses at the forefront of digital transformation and customer-centric financial services. In today’s banking and finance landscape, Generative Artificial Intelligence (Gen AI) is a game-changing catalyst for transformation. Far beyond traditional data processing, Generative AI generates insights, solutions, and opportunities that redefine the financial sector. Yet we’re still in the early innings of cloud-based AI’s impact on financial services and in society more broadly. This is akin to the flip-phone phase with the touchscreen era right around the corner.

You can foun additiona information about ai customer service and artificial intelligence and NLP. Krishi is an eager Tech Journalist and content writer for both B2B and B2C, with a focus on making the process of purchasing software easier for businesses and enhancing their online presence and SEO. Businesses, on the other hand, can process ‘big data’ to make prediction models that can forecast demands and help personalize the customer journey. Since predictive AI can analyze all data about a given consumer, it can quickly identify red flags in the financial history of a borrower.

Data sharing does not apply to this article as no datasets were generated or analysed during the current study. “Don’t ask Generative AI for knowledge,” the policy instructs, nor for decisions, incident reports or generation of images or video. Also prohibited is use of AI in any applications that impact the rights or safety of residents. So in this article, we’ll explore the role of AI agents in transforming enterprise operations, diving into how these advanced systems will drive the next phase of generative AI.

User Experience

All that the customer has to do is choose the proposal that best fits his/her needs and tap a single button. Personalized offers created by AI allow connections with customers on an emotional level, rather than annoying them with tons of useless product description and information overload. This would provide not only an amazing experience for the users but also a key factor that so many financial services of today lack─speed.

If you are inspired by successful generative AI use cases in banking, let’s chatand schedule a discovery session where we could discuss potential applications and limitations for your specific scenario. Banks are expected to continue investing in generative AI models and testing them over the next 2-5 years. In the short term, banks will likely focus on incremental innovations—small efficiency gains and improvements based on specific business needs. Employees will maintain an oversight role to ensure accuracy, precision, and compliance as the technology matures.

Generative AI can identify opportunities to streamline internal processes, improving banks’ operational efficiency and contributing to dynamic workflow optimization. Classifying documents, processing applications, verifying accounts, and finally, opening accounts are other areas where generative AI is used. Still, generative AI is needed to understand and process the unstructured data in documents with varied formats. Document classification and extraction of relevant information from different financial documents is where generative AI is needed. In the digital age, the one-size-fits-all approach no longer works as customers demand and are surrounded by a more personalized experience. As conducted in a study by Wunderman, 63% of consumers state that the best brands are the ones that exceed expectations

throughout the customer journey.

AI software would only require some regular maintenance as opposed to vacations, breaks, the risk of human error and the demand for raises. Banks are already seeking ways to optimize the capabilities of AI chatbots and voice assistants so that it would be possible to solve almost any customer inquiry without a living person in sight. AI can help banks to identify and manage risks by analyzing data and providing insights in real time. AI can help identify potential fraud by analyzing large amounts of data and identifying patterns that may indicate suspicious activity, and take appropriate action to prevent losses. This can save time and resources for the bank, and reduce the risk of financial

losses. It can be used to create different types of applications such as mobile, desktop, web, cloud, IoT, machine learning, microservices, game, etc.

Gen AI can act as an assistant or a coach to employees by helping them do their job more efficiently and ultimately enabling them to focus on strategic, high-impact activities. For example, coding assistance and generation, such as Codey, which is a family of code models built on PaLM 2, can dramatically increase programming speed, quality, and comprehension. Using gen AI can help address some of the most acute talent issues in the industry, such as software developers, risk and compliance experts, and front-line branch and call center employees. Data quality—always important—becomes even more crucial in the context of gen AI. Again, the unstructured nature of much of the data and the size of the data sets add complexity to pinpointing quality issues. Leading banks are using a combination of human talent and automation, intervening at multiple points in the data life cycle to ensure quality of all data.

AI use cases in the banking and finance industry

ChatGPT is a language model that uses natural language processing and Artificial

Intelligence (AI) machine learning techniques to understand and generate human-like responses to user queries. Making part of dedicated digital assets, generative AI algorithms can improve financial forecasting by analyzing historical data and current market conditions, providing more accurate and timely predictions. Financial institutions can leverage such tools for strategic planning processes and continuously train AI models with the latest data to ensure relevance and accuracy in predictions. AI-powered risk models continuously monitor transaction patterns, market trends, and regulatory changes to detect anomalies and mitigate risks in real-time.

So, below we highlight several significant risks and challenges that financial institutions must carefully navigate to achieve success with AI in banking and finance. AI can assist employees by providing instant access to information, automating routine tasks, and generating insights, allowing them to focus on more strategic activities. In the future, banks should adopt a hybrid approach where AI tools augment human capabilities and implement training programs to help employees effectively use AI tools and understand their outputs. To improve customer experience and enhance their support capacity, the bank collaborated with McKinsey to develop a generative AI chatbot capable of providing immediate and tailored assistance.

Given that gen AI is still a relatively new approach to banking, it does bring with it its own set of challenges that cannot be overlooked. Preventing money laundering and complying with regulatory requirements is a paramount concern for banks. Generative AI is proving to be a formidable ally in enhancing Anti-Money Laundering (AML) practices. Explore more on how generative AI can contribute to software development and reduce technology costs, helping software maintenance. A conditional generative adversarial network (GAN), a generative AI variant, was used to generate user-friendly denial explanations. By organizing denial reasons hierarchically from simple to complex, two-level conditioning is employed to generate more understandable explanations for applicants (Figure 3).

How banks are using generative AI

Explore the latest trends and applications of RPA in the pharmaceutical industry. Learn how RPA is improving efficiency, productivity, and accuracy in drug discovery, clinical trials, and more. Java is a popular and powerful programming language that is widely used in a variety of applications, including web development, mobile app development, and scientific computing.

Especially in financial services, where every service or product starts with a contract, terms of service, or other agreement. Gen AI is particularly good at discovering and summarizing complex information, such as mortgage-backed securities contracts or customer holdings across various asset classes. Capabilities such as foundation models, cloud infrastructure, and MLOps platforms are at risk of becoming commoditized, given how rapidly open-source alternatives are developing.

Two scenarios are shown for early and late adoption of automation, and each bar is broken into the effect of automation with and without generative AI. The addition of generative AI increases CAGR by 0.5 to 0.7 percentage points, on average, for early adopters, and 0.1 to 0.3 percentage points for late adopters. In the overall average for global growth, generative AI adds about 0.6 percentage points by 2040 for early adopters, while late adopters can expect an increase of 0.1 percentage points. For most of the technical capabilities shown in this chart, gen AI will perform at a median level of human performance by the end of this decade. And its performance will compete with the top 25 percent of people completing any and all of these tasks before 2040. In this visual Explainer, we’ve compiled all the answers we have so far—in 15 McKinsey charts.

GenAI voice assistants can now automate a high portion of incoming queries and tasks with exceptional intelligence, accuracy and fluidity. This evolution has not only improved the quality of customer interactions, but also expanded the range of services that can be automated. However, the deployment of generative AI in banking comes with its challenges, including data privacy concerns and the need for regulatory compliance. Financial institutions must ensure that their AI systems are transparent, secure, and aligned with industry standards to maximize the benefits of this transformative technology. Moreover, this technology significantly enhances customer experiences by ensuring services are closely tailored to individual needs and preferences.

Built on 8+ years of domain-specific collective intelligence across every channel, the Voice Assistant has exceptional understanding, allowing it to accurately interpret and respond to a wide range of industry queries. It saw its call containment rate soar from 25% when using a non-AI-powered IVR solution, to 75% with interface.ai’s GenAI Voice Assistant. This blog delves into the most impactful Generative AI use cases in banking, showing GLCU’s success and why Generative AI in banking is becoming indispensable. Of course, working with Generative AI in the banking sector has its challenges and limitations.

Analyzing transaction data, identifying fraud patterns, and enhancing models to detect and prevent fraud are where the payment industry and banking industry will invest, which will help them stay ahead of emerging fraud threats. The future banking user experience should be fully personalized and able to come up with solutions that fit each customer’s specific needs in specific circumstances, right when the customers need it. In the future banking marketplace, users don’t have to browse a long list of financial products. Instead, using Open Banking APIs, Light Bank itself will choose the right solution from hundreds of products delivered by third-party providers. Artificial Intelligence

prepares a pre-approved personalized offer in just a few seconds by scoring users’ financial profiles.

Currently, GenAI in banking is primarily used in the back office where it can easily and effectively integrate with simpler workflows. The technology is often focused on automating critical but repetitive processes, including fraud detection, security and loan origination and enhancing the automated customer service experience. GenAI is already driving efficiency and, as McKinsey pointed out, increased productivity is the primary way it will deliver those billion- dollar returns. In line with approaching generative AI for innovation, banks are expected to utilize the technology to improve efficiency in existing and older AI applications. Just like that, automating customer-facing processes creates digital data records that generative AI can use to refine services and internal workflows.

The staff had reported a 50% increase in productivity rate during the trial period. So let us elaborate on how the traditional banking experience can be transformed into a highly differentiated, secure, and efficient service by the convergence of generative AI and banking. These most promising generative AI use cases in banking, with some real-life examples, demonstrate the potential value arising from the technology. Among the financial institutions we studied, four organizational archetypes have emerged, each with its own potential benefits and challenges (exhibit). QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

While they offered 24/7 assistance with an IVR system, it lacked functionality and contextual-understanding that restricted the volume of calls it could handle, and the quality in which it managed them. Some financial institutions like mortgage brokers or investment companies provide financial advice to their customers using gen AI technology. This can be one of the best Generative AI use cases for financial service companies. Such financial advisors and businesses can combine human expertise with the power of AI to give consumers more comprehensive and customized financial plans. Generative AI can help banks to analyze market trends and optimize investment portfolios. These models can determine potential risks and opportunities, enabling banks to make data-driven investment strategy decisions.

Responsible use of gen AI must be baked into the scale-up road map from day one. Naturally, banks encounter distinct regulatory oversight, concerning issues such as model interpretability and unbiased decision making, that must be comprehensively tackled before scaling any application. Leaders must acquire a deep personal understanding of gen AI, if they haven’t already. Investments in executive education will equip them to show employees precisely how the technology and the bank’s operations connect, thereby generating excitement and overcoming trepidation. Financial organizations must adopt a cautious, responsible approach to integrate Generative AI. With proper mitigation strategies, like robust data governance, rigorous testing and validation, prioritization of transparency and explainability, and an ethical AI framework, banks will be able to maintain client trust and safety.

With a hyper-intelligent understanding of the context and specifics of each inquiry, interface.ai’s Voice AI ensures that members receive accurate and relevant responses quickly. The ability to handle tasks has further boosted member satisfaction, as members can now manage their finances at any time of the day, instantly. Here at Aisera, we offer Generative AI tools tailored to different industries, including the financial services and banking industries. Like all businesses, banks need to invest in targeted marketing to stand out from the competition and gain new customers. It takes a lot of deep customer analysis and creative work, which can be costly and time-consuming. In short, Generative Artificial Intelligence can look to the past to help banks make better financial decisions about the future and create synthetic data for robust analyses of risk exposure.

While some financial institutions are adopting generative AI tools at a breakneck pace (though mostly as pilot projects on a small scale), corporate implementation of Gen AI tools is still in its infancy. For the majority of banking leaders, the question of how and where generative AI could deliver the biggest value still stands. We recently conducted a review of gen AI use by 16 of the largest financial institutions across Europe and the United States, collectively representing nearly $26 trillion in assets. Our review showed that more than 50 percent of the businesses studied have adopted a more centrally led organization for gen AI, even in cases where their usual setup for data and analytics is relatively decentralized. This centralization is likely to be temporary, with the structure becoming more decentralized as use of the new technology matures.

The technology is not yet at a state where banks can have sufficient confidence to hand over risk and compliance tasks fully. The advent of generative AI in the banking industry is not about technology evolution—generative artificial intelligence is set to redefine the very essence of banking by shaping entirely new business models. The impact Gen AI has on the banking sector is immense across literally all banking functions, especially in terms of banking operations and decision-making.

There are more areas where Generative AI will be helping financial institutions, banks, and customers. Generative AI introduces complexities related to model interpretability, explainability, and ethical considerations, which must be addressed. Person-specific marketing and offers based on a person’s changing preferences and behavior are feasible due to AI’s generative, learning, and enhancing capabilities. Generative AI is specifically needed to dynamically generate content based on changing trends, market conditions, geographical conditions, customer interactions, and feedback. Another challenge is training ChatGPT to understand the language and terminology specific to the banking industry.

This design change reflects the growing trend of users seeking a more intuitive and search-engine-like experience, aligning with the increasing popularity of generative tools. Another use case is to provide financial product suggestions that help users with budgeting. For instance, the LLM-powered banking chatbot automatically transfers a precise amount of every pay cheque into an account and potentially sets alerts for when a definite sum of money is spent. By scrutinizing a consumer’s unique objectives and risk appetite, it suggests customized investment recommendations. This goes beyond generic advice, ensuring that tips align with individual needs and preferences, ultimately enhancing the customer’s journey.

Using this data, AI can generate highly personalized marketing campaigns and product recommendations tailored to individual customers. Using this, banks can enhance customer satisfaction by offering round-the-clock support, reducing operational costs, and improving response times. Furthermore, chatbots can collect valuable customer data, enabling banks to better understand their clientele and tailor services accordingly. Another example of using predictive AI is small business lending software that incorporates AI-driven lending intelligence. Abrigo Small Business Lending Intelligence powered by Charm provides loan rating risk scores, the probability of default, and how the score was calculated. The engine leverages self-learning AI to continuously monitor a wide range of current and historical data, loan performance, accounting, and macroeconomic data from more than 1,200 institutions.

Additionally, this technology can predict client responses and adjust strategies in real-time, optimizing the process and ensuring compliance with regulations. From there, it can split your leads into segments, for which you can create different buyer personas. That way, you can tailor your marketing campaigns to different groups based on market conditions and trends. In addition, Generative Artificial Intelligence can continually mine synthetic data and update its detection algorithms to keep up with the latest fraud schemes. This proactive approach helps banks anticipate fraudulent behavior before it happens.

Banking and finance emerged as some of the most active users of this earlier AI, which paved the way for new developments in ML and related technologies. These include reshaping AI customer service, that employs AI for enhanced fraud detection, using machine learning to predict financial trends, and customizing banking services for individual needs. Join us as we unravel how these technologies are shaping the future of finance.

CIB marketers can also use the new tools to automatically summarize a bank’s knowledge and use it to create viable marketing content, such as market recaps, research reports, and pitch books. A leading investment bank, for example, has built a gen AI tool to help analysts write first drafts of pitch books. The analyst uploads all the relevant documents and then queries the chatbot to ensure it has the material it needs. Then, https://chat.openai.com/ the analyst can instruct the tool to produce many of the slides that are typically needed and many others that reflect the specifics of the proposed investment. The tool saves analysts about 30 percent of the time they used to spend creating pitchbooks. For many banks that have long been pondering an overhaul of their technology stack, the new speed and productivity afforded by gen AI means the economics have changed.

Furthermore, the integration of generative AI with existing banking systems will streamline operations, reduce costs, and improve decision-making processes. As banks continue to adopt and refine this technology, they will be better equipped to meet the evolving needs of their customers and maintain a competitive edge in the financial industry. Generative AI is poised to revolutionize the banking and financial sectors, offering innovative solutions to enhance operational efficiency and customer experiences. This advanced technology, capable of processing and interpreting vast amounts of data, enables banks to automate complex tasks, provide personalized services, and detect fraudulent activities with greater accuracy.

It can speed up software development, speed up data analysis, and make lots of customized content. It’s expected that Generative AI in banking could boost productivity by 2.8% to 4.7%, adding about $200 billion to $340 billion in revenue. This mindset isn’t surprising given that the banking industry can sometimes be slow to adopt new technologies, but financial institutions that hesitate on GenAI generative ai banking use cases are leaving money on the table and will find themselves in the minority. According to Temenos, 33% of bankers are currently using banking AI platforms for developing digital advisors and voice-assisted engagement channels. In just two months after its launch, GPT-3-powered ChatGPT has reached 100 million monthly active users, becoming the fastest-growing app in history, according to a UBS report.

Customers can effortlessly track spending patterns, monitor subscriptions, and manage payments. With this support, consumers make informed decisions and choose the card that best suits their needs. Ultimately, AI-powered systems provide a convenient and efficient way for customers to find answers to all of their questions. The adoption of Generative AI in the banking industry is rapidly gaining momentum, with the potential to fundamentally reshape numerous operations.

Gen AI isn’t just a new technology buzzword — it’s a new way for businesses to create value. While gen AI is still in its early stages of deployment, it has the potential to revolutionize the way financial services institutions operate. In today’s rapidly evolving landscape, the successful deployment of gen AI solutions demands a shift in perspective—that is, starting with the end user experience and working backward.

This proactive approach improves compliance with regulatory requirements and enhances overall risk mitigation strategies, safeguarding the financial stability of institutions and increasing trust among stakeholders. While traditional AI has come a long way in improving efficiency and decision-making in the banking sector, it may have limitations when dealing with unstructured data, natural language understanding, and complex contextual analysis. Generative AI technologies provide a range of state-of-the-art capabilities that have the potential to address these limitations and go even further.

These generated examples can help train and augment machine learning algorithms to recognize and differentiate between legitimate and fraudulent patterns in financial data. Utilizing generative AI allows financial companies to create tailored financial products based on individual customer profiles and behaviors, leading to higher customer engagement and satisfaction. Banks can integrate the technology into their digital solutions to analyze customer data and market trends and develop innovative and highly personalized financial products. Generative AI-powered tools automate the creation of comprehensive financial reports by analyzing vast amounts of data and generating detailed narratives. For instance, a bank might use AI to interpret commercial loan agreements and generate financial summaries. This application saves time, reduces human error, and ensures that stakeholders receive accurate and timely financial insights, allowing financial analysts to focus on more strategic tasks.

An example of a use case for predictive AI is Signature Bank of Georgia’s addition of AI-driven check fraud detection software that finds fraud faster. The software evaluates over 20 unique features of each check coming in to provide financial institutions with a risk score indicating the probability of a fraudulent check. Banks and credit unions want to serve their clients better and improve their services and products. Yet 30% of financial services leaders ban the use of generative AI tools within their companies, according to a recent survey by American Banker publisher Arizent. For more on conversational finance, you can check our article on the use cases of conversational AI in the financial services industry. For the wide range of use cases of conversational AI for customer service operations, check our conversational AI for customer service article.

Corey also leads Q2’s AI Center of Excellence, enabling the organization to use artificial intelligence tools, ethically and responsibly, to better serve our customers, partners, and people. These models can adjust portfolios in real-time based on changing market conditions and emerging opportunities. This dynamic approach to wealth management allows banks to maximize returns while managing risk effectively. Generative AI models can analyze vast amounts of customer data, including transaction history, browsing behavior, and demographic information.

Financial institutions using more dispersed approaches, on the other hand, struggle to move use cases past the pilot stage. We have found that across industries, a high degree of centralization works best for gen AI operating models. Without central oversight, pilot use cases can get stuck in silos and scaling becomes much more difficult.

Generative AI models can analyze massive volumes of transaction data, customer profiles, and historical patterns to identify suspicious activities. These models not only detect known money laundering techniques but also adapt to evolving schemes, ensuring banks stay ahead of criminal tactics. Generative AI models, when fine-tuned properly, can generate various scenarios by simulating market conditions, macroeconomic factors, and other variables, providing valuable insights into potential risks and opportunities. Considering the challenges and limitations described above, the integration of generative AI solutions into financial operations requires thorough strategic planning. Moreover, with each business case being unique and sophisticated, the decisions related to AI enablement as well as the results expected from technology adoption always make a difference. Currently, OCBC Bank is expecting this in-house AI-based solution to help their 30,000 employees make risk management, customer service, and sales decisions.

Consider securities services, where low margins have meant that legacy technology has been more neglected than loved; now, tech stack upgrades could be in the cards. Even in critical domains such as clearing systems, gen AI could yield significant reductions in time and rework efforts. They can improve their competitiveness in client servicing by using the technology to write documents that are currently produced by hand. And they can tap tools such as Broadridge’s BondGPT2For more, see “LTX by Broadridge Launches BondGPTSM Powered by OpenAI GPT-4,” Broadridge press release, June 6, 2023. To offer investors and traders answers to bond-related questions, insights on real-time liquidity, and more. However, the real holy grail in banking will be using generative AI to radically reduce the cost of programming while dramatically improving the speed of development, testing and documenting code.

This powerful technology is reshaping how we learn and teach, offering tools that make education more personalized and effective. As reported by HolonIQ, the global ed-tech market is projected to hit $404 billion by 2025, mostly thanks to advancements in AI. Bank M&A topics will include balance sheet considerations for both the acquiring and acquired financial institutions such as deposits, capital adequacy, credit quality and more. Information around regulatory preparations and concerns as well as credit risks will also be addressed. To provide customized proposals for each customer, AI could be used for a more accurate customer credit scoring based not only on the user’s bank’s profile and credit history, but also social profiles and offline activity. This would allow the bank to generate

a personalized proposal even before the user has requested it.

Generative AI use cases in banking are diverse and impactful, including enhanced customer service, fraud detection, regulatory compliance, and predictive analytics. At the same time, AI solutions often come with privacy risks that companies should take seriously from the outset. Traditionally, credit risk assessment relied on historical data and statistical models.

Evaluate the quality, security, and reliability of existing data repositories. Ensure adequate storage capacity and data accuracy necessary for developing and training AI solutions. Address any gaps in data infrastructure to support the implementation of generative AI technologies effectively. Beyond any doubt, the use of generative AI in banking is poised to bring both expected and surprising changes, leading to an evolution and expansion of AI’s role in the sector. However, significant changes from generative AI in banking will require some time. Additionally, Citigroup plans to employ large language models (LLMs) to interpret legislation and regulations in various countries where they operate, ensuring compliance with local regulations in each jurisdiction.

AI can be used to analyze historical data and make predictions about future customer behavior, which can be used to optimize products and services. Sixty-six percent of banking executives say new technologies will continue to drive the global banking sphere for the next five years. They point toward AI, machine learning, blockchain or the Internet of Things (IoT) as having a significant impact on the

sector, according to Temenos.

Like utilizing Generative AI in Insurance for fraud detection, banks can use it to track transactions in terms of location, device, and operating system. It can then flag any anomalies or behavior that doesn’t fit expected patterns. From there, bank personnel can review the suspicious behavior and decide if it deserves further investigation. That way, banks don’t need to comb through transactions manually, which takes longer and is prone to human error. A table shows different industries and key generative AI use cases within them.

• Within the technology’s first few months, McKinsey research found that generative AI (gen AI) features stand to add up to $4.4 trillion to the global economy—annually.
• These tools can help with code translation (for example, .NET to Java), and bug detection and repair.
• Another example of using predictive AI is small business lending software that incorporates AI-driven lending intelligence.
• Partner with Master of Code Global to gain a sustainable competitive advantage.

Looking at the financial-services industry specifically, we have observed that financial institutions using a centrally led gen AI operating model are reaping the biggest rewards. As the technology matures, the pendulum will likely swing toward a more federated approach, but so far, centralization has brought Chat GPT the best results. It excels in finding answers in large corpuses of data, summarizing them, and assisting customer agents or supporting existing AI chatbots. For example, in this video, we explore how gen AI can speed up credit card fraud resolution — a win-win for customers and customer service agents.

An app that provides a contextualized experience should be able to predict the exact moment when a user needs a specific product and provide it by combining big data with behavior-based predictive analytics. The data already available to the incumbents could

be used to provide personalized offers based on the user’s purchasing and financial behavior even before the user has requested it. It’s predicted that, in the upcoming years, AI will completely replace most of the jobs in banking and other industries.

Banks must provide relevant training data and integrate the model with their existing systems to ensure that it can provide accurate and appropriate

responses to user queries. Despite the inspiring prospects that AI technology opens up for improving the customer experience in banking, implementing generative AI into banking products can pose some challenges. One of the main challenges is safeguarding the security and privacy of

customer data. Banks must ensure that the chat interface is secure and that sensitive data is protected from unauthorized access or disclosure. Wealth managers can provide clients with more personalized investment strategies and asset allocations, leading to improved client satisfaction and loyalty.