Automating Personalized Bank Product and Service Usage Reporting by Segment

The solution of automating reports on the usage of bank products and services for each partner or specific customer group is the answer to this complex and resource-intensive operation. Automation technology is shaping the future of bank management and business operations.

Business Process Characteristics and Traditional Operational Barriers

The process of monitoring and reporting on the usage of bank products and services by specific customer groups or partners is an indispensable part of financial institutions’ business and risk management strategies. However, the unique nature of this operation presents many significant challenges when implemented manually.

Complex and Diverse Business Characteristics

• Detailed and Complex Customer Group Definitions: Banks often classify customers into many small groups based on very specific criteria to serve business strategies and targeted marketing.
  • Example:
    • The group of employees (CBNV) of large and medium-sized corporate clients: This is a potential customer group, often with stable income, needing products like deposits, consumer loans, and credit cards with special offers.
    • The group of employees of schools, hospitals, and administrative units: This group has specific income characteristics, is stable, and often enjoys special preferential policies from the bank through cooperation with the parent unit.
    • The group of employees from FDI Enterprises: This group has the potential to use international products, foreign currency payments, or needs for home loans in large urban areas.
    • The group of families and bank employees: This group can enjoy internal policies or special benefits based on relationships.
• Linking to Specific Partners (Dealer Code): Besides customer segmentation, monitoring business performance must also be linked to specific partners (e.g., retail dealers, brokerage firms, real estate developers). Each partner can have their own agreements on products, commission policies, and sales targets.
• Massive Volume of Customers and Transactions: Especially in the retail banking sector, the number of customers can reach millions, and each customer might use multiple products and perform hundreds of transactions per month. This creates a massive volume of data that needs to be processed.
• Large and Complex Input Information: To generate a complete and accurate report, data needs to be aggregated from countless different sources:
  • Core Banking System (account information, deposit and loan transactions).
  • Card Management System (card information, card transactions, outstanding balance).
  • CRM System (detailed customer information, segmentation, interaction history).
  • Partner Management System (information on dealer codes, cooperation agreements, policies).
  • Data Warehouse/Data Lake (aggregated and historical data).

Traditional Operational Barriers

With these characteristics, when this operation is performed manually, banks face numerous challenges, becoming a “bottleneck” that hinders efficiency and competitiveness.

• Heavy and Costly Manual Operations:
  • Multi-source data extraction: Employees have to access multiple separate systems, each with different interfaces and extraction procedures. This consumes a lot of time.
  • Manual aggregation and reconciliation: Extracted data is often in different formats (Excel, CSV, PDF reports). Employees must perform complex copy-paste, VLOOKUP, and pivot table operations to aggregate and reconcile, which is prone to errors.
  • Verification: The process of cross-checking and verifying data integrity between sources is very labor-intensive and time-consuming, especially when inconsistencies arise.
  • Actual statistics: According to a Forrester survey, financial institutions spend up to 60-80% of their employees’ time on such manual, repetitive tasks. An EY report indicates that automation can reduce operational costs by 25% to 40% in the banking industry.
• Prone to Errors and Inaccuracy:
  • Human error: When manually processing a large volume of data, data entry, copying, or logic errors in calculation formulas are unavoidable.
  • Lack of consistency: Each employee might have a different approach to data aggregation and formatting, leading to inconsistencies in reports and making it difficult to compare over time.
  • Consequence: Errors in reports can lead to incorrect business decisions, revenue loss, or even compliance risks.
• Compliance and Data Security Risks:
  • Manually handling sensitive customer data poses risks of information security breaches and violations of data protection regulations (e.g., GDPR, State Bank of Vietnam regulations).
  • The lack of transparency and a clear audit trail in manual processes increases compliance risks.
• Scalability Challenges:
  • When the number of customers, products, or partners increases, the workload also increases exponentially. Hiring more staff to handle this pressure is not only costly but also inefficient.
  • The ability to generate timely reports to support sales campaigns or react quickly to the market is limited.

These barriers not only reduce operational efficiency but also hinder the bank’s ability to leverage data for strategic decisions, directly affecting its competitiveness in the market. This is precisely why automation solutions are more urgent than ever.

Describing the Business Process After Applying Automation for Product & Service Usage Reporting by Segment

An automated solution for analyzing and reporting on the detailed usage of products like deposits, loans, cards, account packages, etc., by specific customer groups or partner codes (dealer code) helps support effective management and drive targeted sales in a scientific and accurate manner. This robot acts as a “superb virtual assistant,” performing complex steps automatically, continuously, and accurately.

Objectives of the Robot System

• Provide a holistic view: The system helps the bank gain a detailed view of the performance of each product and service used by customer groups or through each specific partner.
• Serve performance management: It provides performance metrics (KPIs) related to sales, revenue, profit, and the engagement level of each customer segment or partner.
• Drive sales and personalization: Based on data and analysis, the system supports identifying opportunities for cross-selling and up-selling, and personalizing marketing campaigns.
• Support strategic decision-making: It provides accurate and timely data for managers to adjust product policies, pricing strategies, or resource allocation.

Detailed Operational Steps of the Robot

The robot’s operation is designed as a fully automated cycle, from data collection to report distribution.

• Step 1: Automated Scheduling & Triggering: The robot is set to automatically activate on a fixed schedule (e.g., daily, weekly, end of month) or based on specific events (e.g., when new data is updated in the Data Warehouse). This ensures reports are always generated in a timely manner without manual intervention.
• Step 2: Multi-System Data Extraction: The robot is configured to securely connect to all necessary source systems:
  • Core Banking System (CBS): It retrieves detailed data on deposit accounts (balance, term, interest rate), loan accounts (outstanding balance, repayment history), payments, and integrated account packages.
  • Card Management System (CMS): It retrieves information on card issuance (credit, debit), card transactions, credit limits, and card debt.
  • Customer Relationship Management (CRM): It extracts customer information, including segment (individual, SME), specific customer groups (employees of schools, hospitals, FDI enterprises), interaction history, and information about dealer codes.
  • Partner Management System: It collects data related to the performance of each partner (dealer code), including sales agreements and commissions.
  • Data Warehouse/Data Lake: It retrieves aggregated and historical data for trend analysis.
  • The robot uses Application Programming Interfaces (APIs) if available, or simulates User Interface (UI) interaction to access and download data from older applications that lack APIs.
• Step 3: Data Normalization & Cleansing: Raw data from multiple sources is often inconsistent in format and may contain errors or missing values. The robot automatically performs:
  • Format conversion: Synchronizes date, currency, and number formats.
  • Duplicate and null value removal: Automatically detects and handles duplicate records or missing data fields.
  • Validity checks: Ensures data complies with business rules (e.g., checking if the customer code exists in the main system).
  • Encoding and anonymization (if needed): To ensure compliance with data security regulations.
• Step 4: Data Aggregation & Linking: This is the key step to creating a comprehensive view. The robot will:
  • Data linking: Uses common key fields (e.g., customer ID, account number, partner code) to link data from different systems into a single, comprehensive dataset.
  • Automated segmentation: Automatically classifies data into predefined customer groups or partners (employees of schools/hospitals, FDI enterprises, dealer codes X, Y, Z, etc.).
  • KPI calculation: The robot performs complex calculations to generate crucial performance indicators such as:
    • Total deposit balances by customer group/partner.
    • Total loan/credit outstanding balance by group/partner.
    • Number of card transactions, transaction value, and card activation rate.
    • Usage rate of features in savings packages.
    • Estimated revenue and profit from each group/partner.
    • New customer rate, customer retention rate.
• Step 5: Report Generation & Visualization: Based on pre-designed report templates, the robot automatically generates detailed and summary reports. These reports can include:
  • Performance summary tables: Provide a quick overview of key metrics for each group/partner.
  • In-depth product analysis: Detailed statistics on each product (deposits, loans, cards) used by each group.
  • Performance comparison: Compare performance between customer groups or dealer codes to identify strengths and weaknesses.
  • Trends and fluctuations: Graphs showing the change in metrics over time.
  • Reports can be exported in various formats like Excel, PDF, CSV, or updated directly to Business Intelligence (BI) tools such as Power BI and Tableau to create interactive dashboards.
• Step 6: Automated Report Distribution: Once the report is finalized, the robot automatically sends the reports to the email addresses of relevant stakeholders (management, sales department, product department, partner management) or saves them to secure network folders, or uploads them to internal portals. This ensures information is delivered to the right person at the right time without manual intervention.

With this automated process, the robot not only completely replaces time-consuming manual tasks but also ensures accuracy, timeliness, and deep analytical capabilities, helping the bank manage effectively and optimize its business strategy.

The Process After Automation

Before automation, the process of generating reports on product and service usage by customer/partner groups was a series of manual, fragmented, and error-prone steps. After implementing the robot reporting system, the process has been streamlined and significantly optimized, leading to superior efficiency.

“Before” Automation Process

• Report request: The sales/product department sends a report request to the operations/analysis department.
• Manual system access: An employee has to manually log in to each system (Core Banking, CRM, Card System, etc.).
• Manual data extraction: Download CSV or Excel reports from each separate system. This process is often slow and prone to errors if the record count is large.
• Manual data merging: Open Excel files and use functions like VLOOKUP, INDEX-MATCH, and Pivot Tables to link and reconcile data from different files. This is the most time-consuming and error-prone step.
• Data cleansing and transformation: Handle null values, inconsistent formats, and data entry errors.
• Manual classification and calculation: Based on manual criteria, classify customers into groups and then calculate metrics (total deposits, loan balances, card transactions, etc.) using complex Excel formulas.
• Report formatting: Arrange data, create charts, and tables according to the required template, which often takes several hours to several days.
• Verification: Cross-check data and calculation results, often requiring multiple rounds of review.
• Report distribution: Manually send the report via email to relevant stakeholders.
• Limitations of the old process:
  • Execution time: Ranges from several days to a week for a complex report.
  • Cost: High due to many employee hours spent.
  • Error risk: Very high due to the human factor.
  • Timeliness: Reports are often delayed, leading to decision-making based on outdated information.
  • Scalability: Limited, difficult to meet multiple report requests simultaneously.

“After” Automation with Robots (Fast, Accurate, Efficient)

After implementing the robot system, the process has been completely transformed:

• Automated scheduling or triggering: The robot is programmed to automatically start the process at a predefined time (e.g., 1 AM daily) or as soon as new data is available in the source systems. No human intervention is needed.
• Robot automatically accesses and extracts data:
  • The robot securely logs in to all systems (Core Banking, CRM, CMS, etc.) using encrypted login credentials.
  • It automatically navigates, executes predefined data queries, and downloads necessary data files at high speed, without errors.
  • Execution time: Reduced from hours to just a few minutes for data collection.
• Robot automatically cleans, merges, and analyzes data:
  • Immediately, the robot processes the downloaded data files: cleaning, normalizing formats, removing duplicates, and linking data from different sources.
  • The robot automatically applies complex business logic to classify customers into specific groups (school employees, FDI enterprises, Dealer Codes X, Y…), and calculates all the necessary KPIs.
  • Accuracy: Reaches a level of nearly 100%, eliminating human errors.
  • Execution time: Reduced from several days to a few dozen minutes for the entire processing and analysis.
• Robot automatically generates and visualizes reports:
  • Based on the processed data and pre-programmed templates, the robot automatically populates the reports, creates tables, and visual charts.
  • Reports are exported in the desired format (Excel, PDF, Power BI) and are ready for distribution.
  • Customization: It’s easy to change report templates or add new metrics just by updating the robot’s configuration.
• Robot automatically distributes reports:
  • The robot automatically sends report emails to a defined list of recipients, saves reports to secure network folders, or uploads them directly to BI dashboards.
  • Timeliness: Reports are available in real-time or within a few hours after data is updated, instead of waiting for days.
• Example of a breakthrough change:
  • Imagine a bank with 500,000 individual customers belonging to various groups and 500 dealer codes.
  • Previously, to generate a monthly report on the usage of 3 main products (deposits, loans, cards) for each group/partner, a team of 3-4 employees could take 5-7 working days.
  • After applying automation, the entire process can be completed in less than 2-3 hours, including bot runtime and final data verification (if needed).
  • Employees are freed from repetitive tasks and can focus on in-depth data analysis, making strategic recommendations, or directly interacting with customers and partners.
  • This transformation isn’t just about speed but also about accuracy, scalability, and, most importantly, shifting from a reactive process to a proactive, data-driven one for decision-making.

Typical Value of the Automated Reporting Solution

Implementing a Robot Reporting system on product and service usage by customer/partner groups brings core values that deeply impact the bank’s operational efficiency and competitiveness.

Optimized Operational Efficiency and Cost Reduction

• Minimized Errors and Enhanced Accuracy: The robot performs tasks with near-perfect accuracy, eliminating human errors in data collection, aggregation, and calculation. This ensures business decisions are made based on reliable information. According to Deloitte, RPA implementation can reduce process errors to less than 1%.
• Significant Time and Resource Savings: Time-consuming, repetitive tasks like data extraction, merging, and formatting are handled automatically by the robot in just minutes or a few hours, instead of days or weeks as before. A Gartner report indicates that RPA can automate up to 80% of repetitive business processes in the financial services industry, leading to a 25% to 50% reduction in operational costs.
• Increased Responsiveness and Timeliness of Information: Reports are generated quickly, potentially daily, hourly, or in real-time. This allows managers and the business team to quickly grasp the situation, identify new trends, and make timely decisions to seize market opportunities or resolve issues.

Driving Revenue Growth and Improving Customer Experience

• Deeper Understanding of Customer Behavior and Needs: The robot collects and analyzes detailed data on how each customer group (employees of schools, FDI enterprises, etc.) or customers from each partner (dealer code) use products and services.
  • Specific analysis: The robot can show that employees of School A frequently use money transfer services but rarely use credit cards, whereas employees of FDI enterprise B have a high demand for foreign currency transactions and home loans. This information helps the bank build a comprehensive and accurate picture of each group’s financial behavior.
• Personalizing Products and Marketing Strategies: Based on the deep insights from the reports, the bank can:
  • Design more suitable products: Develop product packages and services that are “tailor-made” for each customer group, maximizing their relevance and attractiveness.
  • For example: Designing a preferential loan package for hospital employees or a flexible savings package for FDI enterprise employees.
  • Build targeted marketing campaigns: Launch marketing and promotional campaigns that target the right audience, increasing conversion rates and reducing wasted marketing spend.
• Enhanced Cross-selling and Up-selling: The robot can automatically identify cross-selling opportunities. For example, a robot could suggest a loan or cash management package to an SME customer who only uses a checking account but has a large cash flow. According to a McKinsey study, personalization can boost sales for financial institutions by 10% to 15%.
• Optimizing Partner Collaboration (Dealer Code): Detailed reports by dealer code help the bank accurately assess the performance of each partner, allowing it to:
  • Adjust commission policies: Ensure commission policies are commensurate with the performance delivered.
  • Provide support and training: Offer timely support and training to underperforming partners.
  • Foster stronger relationships: Build more robust relationships based on transparent data and shared goals.

Enhanced Risk Management and Compliance Capabilities

• Minimized operational risk: Automating manual tasks significantly reduces the risk of human error, data inaccuracies, and procedural violations.
• Ensured regulatory compliance: The automated process can be designed to integrate compliance rules (e.g., AML/KYC regulations, customer data security), ensuring all reports and activities meet legal requirements.
• Transparency and auditability: Every action of the robot is logged, providing a clear audit trail that makes it easy to track and verify.

In summary, the Automated Product and Service Usage Reporting solution is not just a tool to help banks save costs but also a strategic leverage to drive revenue growth, personalize customer experience, and enhance overall management capabilities in the digital era.

Recommendations from Technology Experts: Elevating the Solution with AI

While applying RPA-based reporting automation has brought breakthrough value, the future of this solution lies in its deeper integration with Artificial Intelligence (AI). Technology experts from leading consulting firms like EY, Forrester, Deloitte, and Gartner all agree that AI will take analysis and personalization to a new level.

General Recommendations from Experts

• Transition from rule-based automation to Intelligent Automation: Consulting firms like EY and Deloitte emphasize that RPA is a foundation, but to achieve maximum efficiency, it must be combined with AI/ML. RPA performs repetitive tasks, while AI provides the ability to “think,” learn, and make decisions based on complex data.
• Invest in Data Foundation: Gartner and Forrester continuously stress the importance of building a robust, clean, and well-structured data infrastructure. AI can only be intelligent when fed with high-quality data. Banks need to invest in modern Data Lakes, Data Warehouses, and data management tools.
• Develop In-house AI and Data Science Capabilities: Experts recommend that banks build a team of AI, data science, and machine learning engineers to be able to develop and customize AI models that suit their specific needs.
• Focus on Specific Business Value: Instead of scattered AI deployment, banks should identify specific use cases where AI can bring the most significant value, starting with problems where efficiency can be clearly measured.

Specific AI Applications to Elevate the Reporting Solution

• Advanced Customer Behavior Analysis with Machine Learning (ML):
  • Detecting hidden patterns: ML can detect complex patterns in product and service usage behavior of customer groups that traditional rules cannot recognize. For example, it can analyze transactions to find factors influencing the decision to use a credit card for specific types of spending.
  • Dynamic customer segmentation: Instead of static segments, ML can create dynamic customer segments based on behaviors and needs that change over time, allowing for continuous personalization of marketing campaigns and products.
  • Predicting future product demand: ML can analyze historical data and external factors (market trends, seasonality, economic news) to predict the future demand for deposits, loans, or cards for each customer group.
• Prediction and Early Warning with AI:
  • Predicting product/partner performance: AI can forecast sales, revenue, and product usage levels for each customer group or dealer code in subsequent periods with high accuracy.
  • Customer churn prediction: By analyzing signals in product usage behavior (e.g., declining deposit balances, reduced card transaction frequency), AI can predict which customers are at risk of leaving, allowing the bank to take timely retention measures.
  • Fraud alerts: AI can identify unusual transactions or product usage behaviors that pose a fraud risk in real-time, helping to prevent losses.
• Personalizing Product Recommendations with Recommendation Systems:
  • Based on an analysis of the behavior and preferences of each customer and similar customers, AI can automatically recommend the most suitable products and services. For example, suggesting a high-interest savings package to a customer with a large balance and low transaction frequency, or recommending a home loan package to a customer with a good credit history and a latent need.
  • Creating “dynamic” offers: AI can provide real-time personalized offers based on a customer’s current behavior (e.g., a fee reduction for ATM withdrawals when a customer is near a partner bank’s ATM).
• Automating Decisions with Agentic AI:
  • Automatic marketing strategy adjustments: Agentic AI can not only make recommendations but also automatically take actions (e.g., sending a promotional email, adjusting notifications in the banking app) when it detects an opportunity or risk.
  • Product portfolio optimization: Agentic AI can continuously evaluate the effectiveness of product packages and automatically suggest adjustments to features, pricing, or even autonomously create new product variations based on market data and customer needs.
  • Automatic response to market fluctuations: When there is a major change from a competitor or an economic trend, Agentic AI can automatically analyze the impact and recommend, or even execute, immediate response measures.
• Data Proving the Potential of AI in Finance:
  • According to Forrester, organizations adopting AI can gain a significant competitive advantage, with 29% of leading financial institutions having already achieved clear benefits from AI implementation.
  • PwC predicts that AI could boost global GDP by up to 14% by 2030, with the financial services sector being one of the biggest beneficiaries due to its ability to improve productivity and personalization.
  • Gartner estimates that by 2025, 50% of financial processes will be automated by AI, underscoring this irreversible trend.

In conclusion, elevating the robot reporting solution with AI is not just a recommendation but an inevitable roadmap for banks that want to maintain and grow their competitive advantage in the future. The combination of the superior automation capability of robots and the analytical, learning intelligence of AI will usher in a new era of smart, customer-centric banking.

Conclusion

In the context of fierce competition and data explosion in the banking industry, managing and reporting on the usage of products and services by customer groups and partners is not just an operational task but a key factor determining strategic success. The barriers from manual processes have been, and continue to be, a source of challenges in terms of costs, accuracy, and responsiveness. However, the emergence of a solution for automating product and service usage reporting has brought about a revolution.

By fully automating the entire process chain from data collection, cleansing, aggregation, and analysis to report distribution, banks can significantly minimize errors, save thousands of working hours, and, most importantly, gain timely, in-depth insights into customer behavior and partner performance. This not only optimizes operations but also serves as a powerful lever for product personalization, driving cross-selling and revenue growth.

Looking ahead, with recommendations from leading experts and proven data from reputable organizations, the deeper integration of AI and Agentic AI into this solution will elevate a bank’s capabilities to a whole new level. From predicting trends and warning of risks to automatically making recommendations and taking personalized actions, AI will transform banks into smart, agile organizations that consistently meet and exceed customer expectations.

Investing in intelligent automation is not just an expense but a profitable investment strategy that helps banks stand firm on the path of digital transformation and sustainable development in the 4.0 era.