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How to Use AI to Optimize Customer Relationships

iStock 1923671267 - Global Banking | Finance

How to Use AI to Optimize Customer Relationships

Written by Venkata Mudumbai

April 2024

In response to evolving customer expectations and increasing competition, it’s essential for financial services organizations to adopt a modern, customer-centric approach to service. Customer relationship management (CRM) systems help organize and automate tasks such as account, contact, opportunity, contract and order management, data collection and analysis, and sales pipeline management. Artificial intelligence (AI) is a pivotal force in CRM, improving efficiency and reducing costs while providing fast, personalized customer experiences. Traditional financial management methods are being replaced by machine learning (ML) algorithms, automating trading and improving data-driven decisions. Technologies such as ML, deep learning (DL), and natural language processing (NLP) can analyze enormous volumes of data in real-time. As AI becomes ubiquitous in everyday life, users are more comfortable with AI-powered services. Meanwhile, regulatory bodies are beginning to adapt to the implications of AI, increasing adoption in the financial sector.

Applications of AI in CRM

Many enterprises are already reaping the benefits of harnessing AI to elevate client interactions. For instance, in 2018, Bank of America introduced an AI financial assistant called Erica to assist users with routine financial tasks. Erica has logged over one billion customer interactions, and Bank of America’s earnings have risen 19 percent in the past five years. ICICI Bank integrated an AI interface into its customer support services, enabling instant responses to user queries. Some ways AI-powered CRMs can enhance financial services operations include:

  • Personalized customer experiences. AI algorithms analyze customer demographics, transaction history, online behavior, and other data points to create detailed customer profiles. Through AI-powered journey mapping, financial institutions can provide highly personalized services, recommendations, and content that meet individual needs, preferences, history, and financial health. Research shows that effective personalization can deliver an ROI of 800 percent on marketing spend while increasing sales by at least 10 percent. Additionally, conversational AI driven by NLP capabilities can communicate with customers, address complaints, troubleshoot problems, and offer personalized suggestions.
  • Improved efficiency and reduced costs. AI can take over repetitive tasks, streamline workflows, and automate actions such as document management and resource allocation, increasing productivity and lowering operational costs. For example, AI can automate the analysis of customer data so that companies can identify trends and patterns more quickly. Additionally, AI-enabled platforms can generate automatic emails, delivering timely and personalized messages based on interactions and context. By automating workflow, service operations, document management, and resource allocation, AI-powered CRM systems reduce the need for manual intervention, contributing to cost reduction and overall profitability.
  • Enhanced fraud detection. AI’s ability to analyze and learn from large datasets enables it to detect patterns and anomalies that may indicate fraudulent activity. ML allows systems to become increasingly agile, helping them adjust to new and evolving cyberthreats. Banks like J.P. Morgan Chase are increasingly integrating AI into their fraud detection systems, helping them identity theft and fraud with a higher degree of accuracy than traditional systems.
  • Automated regulatory compliance. AI helps financial institutions ensure compliance with ever-evolving regulatory requirements by automatically monitoring transactions and flagging any that may violate regulations. In addition to reducing the possibility of human error, AI ensures reporting accuracy and helps organizations avoid penalties and maintain untarnished reputations.
  • Improved strategic decision making. AI’s data-crunching capabilities can provide comprehensive risk assessments based on historical data and market trends, informing stronger, data-driven decisions. Insights can be leveraged to identify potential leads, direct marketing initiatives, and monitor campaign effectiveness, adjusting efforts in real-time. Predictive analytics identify future trends, marketplace dynamics, and customer behaviors, helping organizations develop new products and services, modify existing strategies, or expand into new markets.
  • Integration with the Internet of Things (IoT). AI-powered CRM systems integrate with IoT devices, such as wearables, homes, and cars, to feed data into client profiles, giving companies a more comprehensive understanding of their customers. Research shows that the market for global banking and financial services for IoT is projected to reach $116 billion by 2026, a dramatic increase from $17.85 billion in 2018. Armed with real-time data about customer behavior, preferences, and feedback, companies can tailor messages and delivery to individuals. Additional applications include smart payment solutions and biometric authentication.

Overcoming challenges in AI implementation

While integrating AI into CRM can bring numerous and remarkable benefits to businesses, some implementation challenges and obstacles remain. AI-powered CRM solutions require significant computational resources, and as the volume of data grows, systems must be able to scale accordingly. This can be managed using containerization technologies like Docker and Kubernetes, which allow for efficient use of resources, isolation of processes, and easy deployment and scaling of AI models. Distributed computing frameworks like Apache Spark that enable data processing and ML also aid in scalability, and cloud-based solutions provide the necessary infrastructure to scale up or down based on demand.

Financial services often require real-time decision-making, posing a challenge for AI models that are slower to process data. Solutions include more efficient algorithms, optimizing existing models, or using approximations to provide quick but reasonably accurate predictions. Additionally, AI models can inadvertently learn biases present in the training data, leading to unfair outcomes. Techniques like disparate impact analysis (DIA) can be used to detect bias in AI models. Bias mitigation algorithms can then be applied to ensure fair decisions.

Over time, the performance of AI models can degrade as the underlying data distribution changes, a phenomenon known as model drift. Regular performance monitoring is necessary to detect drift. Once detected, the model can be retrained on the new data or updated using online learning techniques. 

Ensuring data privacy and consumer trust in AI systems

It is critical to establish strong data governance and privacy measures before AI solutions are implemented. Prioritizing compliance with regulatory frameworks such as those established by the General Data Protection Regulation (GDPR) and the Sarbanes-Oxley (SOX) Act is vital. These frameworks vary by region, so it’s important for global companies to ensure compliance with all applicable standards.

Maintaining customer trust and privacy is an ongoing, collaborative process. Strong, transparent policies ensure customers understand how their data is collected and used. Clear dispute regulation policies and incidence response plans help resolve conflicts satisfactorily for all parties. It is also imperative that enterprises create a culture of responsiveness to customer feedback and complaints. Regular audits by third-party organizations and collaborations with data protection authorities help hold organizations accountable and ensure that user privacy and data protection are prioritized consistently.

Leadership can ensure the successful implementation of AI in CRM by creating an environment that encourages experimentation, innovation, transparency, regulatory compliance, and accountability. Adherence to industry standards is critical. Successful AI adoption involves an organization-wide AI-first approach that includes continuous evaluation, improvement, and innovation.

Monitoring performance and customer satisfaction

When implementing AI-based CRM systems, continuously monitoring and evaluating customer satisfaction are crucial. User sentiment can be measured through a variety of tools, including:

  • Composite customer satisfaction (CSAT). This aggregate measure of customer satisfaction is calculated from user responses to surveys after services were provided. CSAT can be used to predict customer retention and is typically expressed as a percentage.
  • Net promoter score (NPS). NPS measures the likelihood that customers will recommend a business based on their responses to a one-question survey. 
  • Customer effort score (CES). This measures how difficult it is for customers to receive resolutions to issues with a company’s product or service. 
  • Customer satisfaction index (CSI). The CSI tracks customers’ opinions on various aspects of a product or service, such as price, flexibility, and quality.
  • System usability scale. This 10-item survey measures various aspects of system usability and learnability.
  • Social media sentiment analysis. NLP measures social media users’ opinions of a brand or product. 
  • User engagement metrics. These metrics track the percentage of active total users during a specified period.
  • User retention. This is the number of customers who continue to use or purchase a given product over a set period. 
  • Product usage analytics. These analytics track and evaluate user data about how users interact with products, including personal, engagement, and behavioral data. 

By measuring key performance indicators (KPIs) and benchmarking against industry standards, leaders can ensure their CRM solutions provide customers with the best value possible. 

The future of AI-centric financial services

To excel in a rapidly changing marketplace, financial institutions will need AI-powered technology frameworks to provide sophisticated, personalized, and unique customer experiences at scale in real time. Success in any digital transformation demands a holistic approach that spans all layers of the organization. To remain relevant, firms will need to adopt comprehensive deployment of AI, embedding it across the entire operation, from the front office to back-end operations. By enabling the technology throughout a diverse set of services, including CRM systems, businesses can transform customer experiences while optimizing performance and maximizing profitability. AI-driven analytics, chatbots, productivity tools, personalization, and language processing can drive increased, higher-quality customer engagement. Meanwhile, AI-powered risk management tools facilitate customer trust and awareness by providing data-driven insights and risk mitigation. The AI-first bank of the future will be characterized by innovation, agility, speed, and scalability, creating stronger value propositions and tangible business results.

About the Author: 

Venkata Mudumbai is a Salesforce technology architect boasting an extensive 20-year tenure within the IT industry. Throughout his career, he has spearheaded the delivery of expansive CRM projects tailored for mega-corporations. Renowned for his expertise in sales, service, and marketing applications, Mr. Mudumbai plays a pivotal role in the seamless integration of artificial intelligence into CRM applications. He holds a master’s degree in information systems and is dually certified as a Salesforce Technology Architect, exemplifying his commitment to professional excellence. For more information, contact [email protected].

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