FinSecure Bank BI Transformation: Network Analysis & AI

FinSecure Bank is a fast-growing financial services company operating across several African countries. They have recently embarked on a major Business Intelligence (BI) transformation programme to better understand customer behaviour, improve fraud detection, and optimise product offerings.

As part of this initiative:

• They are exploring advanced network analysis to uncover hidden relationships in financial transactions and detect fraudulent activities.
• They are implementing predictive models and machine learning algorithms to support strategic decision-making across departments.
• The bank wants to deploy a new BI platform that allows for interactive dashboards, reporting, and real-time analytics for executives.
• Leadership is also interested in applying business analytics tools to customer segmentation, churn prediction, and risk profiling, with a strong focus on cognitive computing and AI-driven insights.

You have been appointed as a Business Intelligence Consultant to guide FinSecure in leveraging advanced BI practices aligned to the latest trends in data science.

QUESTION ONE                              [25]

• Explain the importance of Network Analysis in modern BI and outline three types of network relationships that FinSecure Bank can uncover to support fraud detection and customer insights.                                                                                                                                (15)
  • Describe the process of developing and deploying predictive models in a BI context. Include the types of predictive models most applicable to FinSecure’s goals.            (10)

QUESTION TWO                               [25]

• Critically evaluate the role of Business Analytics in improving strategic decision- making. Illustrate with examples how FinSecure can use business analytics for customer segmentation and risk management.                                                                                                                                    (15)
  • Discuss the relationships between Cognitive Computing and other related domains such as Artificial Intelligence, Big Data Analytics, and the Internet of Things (IoT). How can these relationships enhance FinSecure’s BI capabilities?                               (10)

QUESTION THREE                        [20]

1. Identify and explain the key components of a modern BI platform and describe how FinSecure can implement an effective BI solution to meet executive and operational need       (12)
   1. Recommend methods for monitoring and evaluating the performance and ROI of BI initiatives within FinSecure Bank.                             (8)

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Business Intelligence Answers: Expert Answers on Above FinSecure Bank Case Questions

Importance of network analysis in business intelligence
When it comes to the importance of network analysis in business intelligence, it is quite useful in detecting hidden links between customers, accounts and transactions. As a result, fraud detection becomes easy and efficient, and it allows for a sound financial ecosystem.
Three types of relationships: This include customer transaction links, customer-customer links and customer product links.
Developing and deploying predictive models
The process in deploying predictive models includes data collection, cleaning, feature engineering, selecting the model, testing and deployment and finally monitoring of the process.
Applicable models: The models applicable includes classification models for detecting fraud, regression models in forecasting product demand, clustering models for customer segmentation.
Role of business analytics in decision making
When you come to the role of business analytics in this decision making, it plays a crucial role in converting raw data into actionable insights for decision making. In the given case scenario of FinSecure, business analytics would be useful in customer segmentation which allows the company in personalised offerings and assists in targeted retention campaigns. With respect to risk management, BI would help FinSecure in predicting scoring models to assess creditworthiness and default risk.
Cognitive computing and related domains
AI helps in intelligent automation whereas Big Data Analytics is useful in handling large transactions, IoT allows for collection of real time data from digital channels, and all of these together can help in fraud detection and high predictive accuracy.
Key components of a modern BI platform
The key components include data integration layer, data warehouse, analytics engine, visualisation tools, and user access and governance.
Monitoring and evaluating BI performance
It can be possible by applying methods such as tracking KPIs, undertaking cost-benefit analysis and continuous performance benchmarking against strategic goals. 

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