Volodymyr Zubchenko: Data analysis and modeling in a modern bank: why data driven solutions provide a reliable competitive advantage?
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Volodymyr Zubchenko: Data analysis and modeling in a modern bank: why data driven solutions provide a reliable competitive advantage? People Analytics Conference 2025 Website – https://pacamp.org Youtube – https://www.youtube.com/@edunomicaorg FB – https://www.facebook.com/pacamporg/
Volodymyr Zubchenko: Data analysis and modeling in a modern bank: why data driven solutions provide a reliable competitive advantage?
- 1. Confidential and proprietary Any use of this material without specific permission of Raiffeisen Bank is strictly prohibited. Data analysis and modeling in a modern bank: why data driven solutions provide a reliable competitive advantage? Volodymyr Zubchenko
- 2. Digital Transformation of Business
- 3. Power BI – one of the most popular modern tools of Business Intelligence
- 4. Using cloud services for Digital Solutions
- 5. 0 500 000 1 000 000 1 500 000 2 000 000 2 500 000 3 000 000 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 comparison of dynamics regular with prepayment direct relationship inverse relationship 0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0% average rate Linear regression model
- 6. Tasks of classification and clustering using data science
- 7. Key tools for data solutions and data science
- 9. Continuous development and improvement of products and services
- 10. 10 Raiffeisen Bank has implemented the Copernicus CRM system of RBI's own development. AI helps to receive and analyze customer information in the following areas: • Preparation for client meetings: AI analyzes internet data about the customer's activities and consolidates relevant news. • Personalization of services: by analyzing financial indicators and available data, AI helps offer personalized banking products and services, providing "best offers" to customers. • Process optimization: AI automates banking processes, reducing costs and increasing efficiency by providing consolidated business analytics for the customer. Copernicus CRM system
- 11. 11 The AI Sales Assistant is integrated into the Copernicus CRM system specifically within the client meeting preparation widget. Bank managers use it to work with international clients. Essentially, it is a separate platform that contains the logic for selecting customer information and a set of key parameters for the search. When preparing for a meeting is initiated, an API request is made to search for news and map with the specified customer parameters. In case of a successful match, the AI provides up to 10 of the latest relevant news articles from open sources with links and outputs a brief summary of the search results to simplify information screening for the user. Copernicus CRM system
- 12. 12 Through the aggregation of information from internal and external sources, AI enables the bank to avoid requesting basic information from the client, receive timely information from news through media monitoring, aggregate data on the profitability of the client's operations, and spend less time preparing for client meetings. In the context of developing tools for meeting preparation, the AI Generated Customer Summary, currently in development at RBI, will in the future also be able to utilize available internal data such as credit limit applications, annual client collaboration reports, and risk reports. AI will consolidate all available data based on pre-defined algorithms and visualize it in a brief summary. Copernicus CRM system
- 14. 14 Leveraging Data for Strategic Decisions Enhanced Decision-Making Data-driven solutions significantly enhance decision-making processes, leading to more effective outcomes and strategies. Data Collection Sources Banks collect vast amounts of data from transactions, customer interactions, market trends, and social media. Actionable Insights By applying advanced analytics, banks can derive actionable insights that inform strategic decisions and identify opportunities.
- 15. 15 Predictive Analytics for Future Trends Forecasting Customer Needs Predictive analytics helps banks anticipate customer needs by analyzing historical data and identifying trends. Fraud Detection Banks utilize predictive models to detect potential fraud by analyzing patterns in transaction data and flagging anomalies. Managing Credit Risks Predictive analytics enables banks to manage credit risks more effectively by evaluating the likelihood of default based on customer data.
- 17. 17 Personalizing Customer Interactions Importance of Customer Experience Customer experience has become a key differentiator in the banking sector, impacting client satisfaction and loyalty. Data-Driven Solutions Data-driven solutions are essential for personalizing and improving customer interactions, allowing banks to respond effectively to client needs. Tailoring Products and Services By analyzing customer data, banks can tailor their products and services to better align with individual preferences and behaviors.
- 18. 18 Targeted Marketing and Product Offerings Personalized Banking Experiences Targeted marketing and customized offerings create personalized banking experiences that enhance customer satisfaction and loyalty. Data Analysis in Banking Data analysis allows banks to predict customer interest in new products, optimizing offer timing for increased engagement. Customer Loyalty and Satisfaction High levels of personalization foster customer loyalty, essential for maintaining a competitive edge in the banking industry.
- 20. 20 Streamlining Operational Processes Importance of Operational Efficiency Operational efficiency is crucial for banks to cut costs and enhance their profitability, leading to sustainable growth. Data-Driven Solutions Implementing data-driven solutions helps streamline operational processes, improving risk management, compliance, and reporting. Automation of Routine Tasks By automating routine tasks, banks can reduce human error and focus on more strategic activities, boosting overall efficiency.
- 21. 21 Optimizing Loan Approvals and Fraud Detection Optimizing Loan Approval Data analysis improves the loan approval process by assessing creditworthiness with greater accuracy and speed. Fraud Detection Enhancement Enhanced fraud detection systems utilize real-time data analysis to identify unusual transaction patterns promptly. Predictive Maintenance Models Predictive maintenance models help minimize downtime in IT infrastructure, ensuring reliable services for customers.
- 23. 23 Assessing and Managing Risks Core Function of Risk Management Risk management is vital for any bank, ensuring stability and compliance in a complex financial landscape. Data-Driven Solutions Data analysis and modeling tools enhance banks' abilities to assess and manage various types of risks effectively. Credit Risk Models Credit risk models predict borrower defaults, allowing banks to adjust their lending strategies proactively. Market Risk Analysis Market risk analysis helps banks understand the effects of market fluctuations on their investment portfolios.
- 24. 24 Ensuring Regulatory Compliance Importance of Compliance Regulatory compliance is vital for banks to avoid penalties and protect their reputation in the industry. Data Analytics Advantage Data-driven solutions empower banks to meet complex regulatory requirements effectively and efficiently. Monitoring for Illicit Activity Compliance monitoring tools analyze transactions, identifying suspicious activities like money laundering.
- 26. 26 Gaining Market Insights Market Data Analysis Analyzing market data enables banks to uncover insights that can lead to new business opportunities and growth. Competitor Performance Benchmarking Benchmarking against competitors helps banks assess their performance and identify areas for improvement. Adapting Strategies Understanding industry trends allows banks to adapt their strategies and remain competitive in the changing market landscape.
- 27. 27 Adapting Strategies to Market Conditions Sentiment Analysis Insights Sentiment analysis of social media helps to gauge public perception of competitors, aiding in strategic marketing efforts. Economic Indicators Monitoring economic indicators allows companies to adapt their investment strategies and product development to current market conditions. Market Trend Analysis Analyzing market trends ensures that businesses remain competitive by adjusting their strategies in response to changing conditions.
- 29. 29 Driving Innovation with Data Insights Importance of Innovation Innovation is essential for banks to remain competitive in the evolving financial sector. Data-Driven Solutions Data-driven solutions provide actionable insights that facilitate the creation of new financial products and services. Understanding Customer Needs Thorough understanding of customer needs allows banks to identify market gaps for effective offerings.
- 30. 30 Developing New Financial Products Customer Spending Behavior Data analysis uncovers trends in customer spending behavior that can inform product development, leading to tailored investment plans. Personalized Financial Products Insights from data analysis can lead to the creation of personalized financial products like custom investment plans and payment solutions. Emerging Technologies in Banking Data-driven insights help identify technologies like blockchain, AI, and machine learning that banks can utilize to improve their services.
- 31. 31 Thank you !