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– ## Assignment3BankingandFinanceReport.docx
**## Assignment3BankingandFinanceReport.docx**
**/**
**ZAINAB KOLLY**
**ASSIGNMNET #3**
**05/24/2026**
**Royal Bank of Canada (RBC): Use of Descriptive and Prescriptive Analytics**
**Introduction**
The Royal Bank of Canada (RBC) is one of the largest banks in Canada and is also recognized globally. RBC serves millions of customers and offers services such as personal banking, wealth management, insurance, investment services, and capital markets. In recent years, the bank has focused on data analytics, artificial intelligence (AI), and machine learning to improve customer service, lower risk, and make better decisions based on data.
Analytics is important in banking because banks deal with large amounts of customer and transaction data every day. RBC uses descriptive analytics to look at past and current business performance. Prescriptive analytics helps the bank recommend actions and make better decisions for the future. These methods use advanced tools such as AI platforms, machine learning, cloud computing, and business intelligence software.
**Descriptive Analytics at RBC**
Descriptive analytics involves looking at past and current data to help organizations understand what has happened. This method helps businesses find trends, track performance, and make reports that support decision-making. Research shows that descriptive analytics is the foundation of business analytics because it changes raw data into useful information.
RBC uses descriptive analytics in many areas of its business. For example, it analyzes customer transactions. The bank reviews millions of transactions each day to learn how customers spend, save, and manage their money. RBC’s AI tool, NOMI, provides customers with insights about their spending and savings. According to RBC, NOMI uses AI and data to help customers manage their finances more effectively.
The bank also uses descriptive analytics to monitor fraud and manage risk. Analysts look at past transaction data to find unusual or suspicious activity. Dashboards and reports help managers keep track of the bank’s performance, customer service quality, and financial risks throughout the company.
RBC uses descriptive analytics in marketing and managing customer relationships. By studying customer demographics, buying habits, and account activity, the bank groups customers and creates targeted products and marketing campaigns. This helps improve customer satisfaction and loyalty.
Some of the common tools and technologies used by RBC for descriptive analytics include:
· Business Intelligence (BI) dashboards
· Data warehouses and cloud databases
· SQL databases
· Tableau and Power BI visualization tools
· AI-driven reporting systems
· Enterprise data management platforms
These technologies give RBC employees real-time reports and clear views of key performance indicators (KPIs). This helps them make faster and better business decisions.
**Prescriptive Analytics at RBC**
Prescriptive analytics does more than describe past events. It recommends what to do next by using data analysis, machine learning, optimization, and business rules to suggest the best decisions. It answers questions such as “What should we do next?” and “What action will produce the best outcome?”
RBC uses prescriptive analytics in areas such as credit decisions, fraud prevention, and personalized banking. For example, its AI model, ATOM, processes billions of transactions. RBC uses ATOM to help with credit decisions, detect fraud, and personalize services by analyzing transaction histories and financial behaviour to recommend loans and financial products.
Fraud prevention is another important use of prescriptive analytics at RBC. These systems watch customer behaviour in real time and suggest actions if they find suspicious transactions. For example, the system might recommend blocking a transaction for a short time, asking for more customer verification, or alerting fraud experts to investigate. These steps help reduce financial losses and improve security.
RBC also uses prescriptive analytics in wealth management and investment services. AI systems study market trends, customer goals, and financial risks to suggest portfolio strategies and investment opportunities. This helps financial advisors give better recommendations to clients.
According to IBSCDC, the Royal Bank of Canada manages different types of risks, such as credit, operational, liquidity, and market risks, using several risk management methods. Analytical models help the bank find the best ways to reduce financial uncertainty.
Some tools and processes used by RBC for prescriptive analytics include:
· Artificial Intelligence (AI)
· Machine Learning algorithms
· Predictive modeling systems
· Optimization models
· Cloud computing infrastructure
· Real-time analytics platforms
· Borealis AI research institute
· Big data processing platforms
The Borealis AI research division at RBC is important for developing advanced AI systems. This team leads research in machine learning, natural language processing, and decision optimization.
Using descriptive and prescriptive analytics gives RBC several benefits, such as improving customer experience with personalized banking services.
1. Faster and more accurate credit approval decisions.
2. Better fraud detection and security monitoring.
3. Reduced operational and financial risks.
4. More efficient business operations and cost savings.
5. Enhanced strategic decision-making using real-time data.
6. Stronger competitive advantage in the financial industry.
Analytics also helps RBC follow responsible AI practices. The bank aims for fairness, transparency, accountability, and data security when creating its AI systems.
**Conclusion**
In summary, RBC uses both descriptive and prescriptive analytics to improve business performance, customer service, and risk management. Descriptive analytics helps the bank understand customer behaviour, track operations, and find trends using past and real-time data. Prescriptive analytics allows RBC to suggest actions, make better decisions, and get better results with the help of AI and machine learning.
RBC’s investments in AI research, big data, and advanced analytics show that today’s banks depend on data-driven decisions. Analytics helps RBC provide personalized services, manage risks, and keep its leading position in the global banking industry.
**References**
1. Royal Bank of Canada. “Artificial Intelligence at RBC.” Available at: RBC Official Website
2. AI Expert Network. “Case Study: How RBC Leads in AI Integration.” Available at: AI Expert Network Article
3. Wealth Professional. “RBC Applies Proprietary AI Model to Credit Decisions and Rewards Data.” Available at: Wealth Professional Article
4. Investopedia. “Understanding Prescriptive Analytics.” Available at: Investopedia Prescriptive Analytics
5. PwC. “Risk Analytics and Prescriptive Analytics.” Available at: PwC Risk Analytics Article
**## Assignment3BankingandFinanceReport.docx**
**/**
**ZAINAB KOLLY**
**ASSIGNMNET #3**
**05/24/2026**
**Royal Bank of Canada (RBC): Use of Descriptive and Prescriptive Analytics**
**Introduction**
The Royal Bank of Canada (RBC) is one of the largest banks in Canada and is also recognized globally. RBC serves millions of customers and offers services such as personal banking, wealth management, insurance, investment services, and capital markets. In recent years, the bank has focused on data analytics, artificial intelligence (AI), and machine learning to improve customer service, lower risk, and make better decisions based on data.
Analytics is important in banking because banks deal with large amounts of customer and transaction data every day. RBC uses descriptive analytics to look at past and current business performance. Prescriptive analytics helps the bank recommend actions and make better decisions for the future. These methods use advanced tools such as AI platforms, machine learning, cloud computing, and business intelligence software.
**Descriptive Analytics at RBC**
Descriptive analytics involves looking at past and current data to help organizations understand what has happened. This method helps businesses find trends, track performance, and make reports that support decision-making. Research shows that descriptive analytics is the foundation of business analytics because it changes raw data into useful information.
RBC uses descriptive analytics in many areas of its business. For example, it analyzes customer transactions. The bank reviews millions of transactions each day to learn how customers spend, save, and manage their money. RBC’s AI tool, NOMI, provides customers with insights about their spending and savings. According to RBC, NOMI uses AI and data to help customers manage their finances more effectively.
The bank also uses descriptive analytics to monitor fraud and manage risk. Analysts look at past transaction data to find unusual or suspicious activity. Dashboards and reports help managers keep track of the bank’s performance, customer service quality, and financial risks throughout the company.
RBC uses descriptive analytics in marketing and managing customer relationships. By studying customer demographics, buying habits, and account activity, the bank groups customers and creates targeted products and marketing campaigns. This helps improve customer satisfaction and loyalty.
Some of the common tools and technologies used by RBC for descriptive analytics include:
· Business Intelligence (BI) dashboards
· Data warehouses and cloud databases
· SQL databases
· Tableau and Power BI visualization tools
· AI-driven reporting systems
· Enterprise data management platforms
These technologies give RBC employees real-time reports and clear views of key performance indicators (KPIs). This helps them make faster and better business decisions.
**Prescriptive Analytics at RBC**
Prescriptive analytics does more than describe past events. It recommends what to do next by using data analysis, machine learning, optimization, and business rules to suggest the best decisions. It answers questions such as “What should we do next?” and “What action will produce the best outcome?”
RBC uses prescriptive analytics in areas such as credit decisions, fraud prevention, and personalized banking. For example, its AI model, ATOM, processes billions of transactions. RBC uses ATOM to help with credit decisions, detect fraud, and personalize services by analyzing transaction histories and financial behaviour to recommend loans and financial products.
Fraud prevention is another important use of prescriptive analytics at RBC. These systems watch customer behaviour in real time and suggest actions if they find suspicious transactions. For example, the system might recommend blocking a transaction for a short time, asking for more customer verification, or alerting fraud experts to investigate. These steps help reduce financial losses and improve security.
RBC also uses prescriptive analytics in wealth management and investment services. AI systems study market trends, customer goals, and financial risks to suggest portfolio strategies and investment opportunities. This helps financial advisors give better recommendations to clients.
According to IBSCDC, the Royal Bank of Canada manages different types of risks, such as credit, operational, liquidity, and market risks, using several risk management methods. Analytical models help the bank find the best ways to reduce financial uncertainty.
Some tools and processes used by RBC for prescriptive analytics include:
· Artificial Intelligence (AI)
· Machine Learning algorithms
· Predictive modeling systems
· Optimization models
· Cloud computing infrastructure
· Real-time analytics platforms
· Borealis AI research institute
· Big data processing platforms
The Borealis AI research division at RBC is important for developing advanced AI systems. This team leads research in machine learning, natural language processing, and decision optimization.
Using descriptive and prescriptive analytics gives RBC several benefits, such as improving customer experience with personalized banking services.
1. Faster and more accurate credit approval decisions.
2. Better fraud detection and security monitoring.
3. Reduced operational and financial risks.
4. More efficient business operations and cost savings.
5. Enhanced strategic decision-making using real-time data.
6. Stronger competitive advantage in the financial industry.
Analytics also helps RBC follow responsible AI practices. The bank aims for fairness, transparency, accountability, and data security when creating its AI systems.
**Conclusion**
In summary, RBC uses both descriptive and prescriptive analytics to improve business performance, customer service, and risk management. Descriptive analytics helps the bank understand customer behaviour, track operations, and find trends using past and real-time data. Prescriptive analytics allows RBC to suggest actions, make better decisions, and get better results with the help of AI and machine learning.
RBC’s investments in AI research, big data, and advanced analytics show that today’s banks depend on data-driven decisions. Analytics helps RBC provide personalized services, manage risks, and keep its leading position in the global banking industry.
**References**
1. Royal Bank of Canada. “Artificial Intelligence at RBC.” Available at: RBC Official Website
2. AI Expert Network. “Case Study: How RBC Leads in AI Integration.” Available at: AI Expert Network Article
3. Wealth Professional. “RBC Applies Proprietary AI Model to Credit Decisions and Rewards Data.” Available at: Wealth Professional Article
4. Investopedia. “Understanding Prescriptive Analytics.” Available at: Investopedia Prescriptive Analytics
5. PwC. “Risk Analytics and Prescriptive Analytics.” Available at: PwC Risk Analytics Article