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Top 5 Use Cases for Graph Analytics in Banks


Graph analytics (aka Deep Network Linkage Analytics like Neo4j) is a powerful data analysis technique that can be used by banks to gain insights into complex financial relationships, identify patterns and trends, and make data-driven decisions.


Here are the top 10 use cases for graph analytics in banks:


Graph analytics is an important tool in helping banks to identify and prevent financial crime, including AML and fraud. By analyzing complex networks of financial transactions and relationships, graph analytics can help banks to identify suspicious patterns and behaviors that may indicate criminal activity, such as money laundering or fraud. This can enable banks to comply with AML regulations and reduce their exposure to financial crime risks. Additionally, graph analytics can be used to investigate financial crime, analyzing the relationships between individuals, accounts, and transactions to identify patterns and anomalies that may indicate criminal activity. This can help banks to detect and prevent financial crime more effectively, reducing losses and protecting their customers and stakeholders.


(2) Customer 360 and Journey


Graph analytics can play a critical role in helping banks to understand their customers better by providing a 360-degree view of their interactions and behaviors. By analyzing data from multiple sources, such as transaction data, customer service interactions, and social media, graph analytics can help banks to identify patterns and relationships that may indicate customer preferences, needs, and behaviors. This can enable banks to tailor products and services to better meet the needs of their customers, providing a more personalized and seamless experience across multiple channels. Additionally, by analyzing the customer journey through the bank's various touchpoints, graph analytics can help banks to identify bottlenecks and areas for improvement, enabling them to optimize the customer experience and improve customer satisfaction. Ultimately, by leveraging the power of graph analytics, banks can gain deeper insights into their customers, drive better business outcomes, and build stronger, more valuable customer relationships.



Graph analytics can be a powerful tool for banks to develop hyper-personalized recommendation engines, particularly in the field of wealth management. By analyzing the relationships between investors, advisors, and investment products, graph analytics can identify patterns and behaviors that may indicate investor preferences and risk tolerance. This can enable banks to offer customized investment strategies and products that are tailored to each individual client, based on their unique financial situation and goals. By leveraging the power of graph analytics, banks can provide a more personalized and responsive wealth management service, enhancing customer satisfaction and loyalty. Ultimately, graph analytics can help banks to build stronger, more valuable relationships with their wealth management clients, driving better business outcomes and delivering greater value to both the bank and its customers.



Graph analytics can play a critical role in helping banks to develop digital twins, which are virtual representations of IT systems that can be used to improve resilience and ensure operational continuity. By analyzing data from multiple sources, including network traffic, system logs, and application performance metrics, graph analytics can help banks to identify patterns and relationships that may indicate potential vulnerabilities or failures in IT systems. This can enable banks to proactively identify and mitigate risks, improving IT resilience and reducing the risk of operational disruption. Additionally, by using graph analytics to create a digital twin of IT systems, banks can simulate different scenarios and test the impact of potential changes or disruptions, enabling them to develop robust continuity plans that can be quickly deployed in the event of an incident. Overall, graph analytics can help banks to improve their IT resilience, reduce the risk of operational disruption, and enhance their overall business continuity capabilities.



Graph analytics can be a powerful tool for banks to manage their talent more effectively, by providing a more comprehensive view of their workforce and identifying opportunities for career development and upskilling. By analyzing data from HR systems, performance metrics, and employee engagement surveys, graph analytics can help banks to identify patterns and relationships that may indicate potential hot skills or emerging talent within the organization. This can enable banks to offer personalized career paths, training opportunities, and development plans that are tailored to each individual employee's strengths and goals, increasing job satisfaction and reducing staff turnover. Additionally, by analyzing data on employee turnover and exit interviews, graph analytics can help banks to identify potential risks and issues, enabling them to take proactive steps to address these concerns and retain their top talent.


In conclusion, graph analytics is a powerful tool that can help banks to improve their operations and achieve better business outcomes. In all of the 5 use cases above, graph analytics can help banks to achieve their strategic goals, improve their competitiveness, and deliver greater value to their customers and stakeholders.





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