A Comprehensive Guide For Implementing Data Analytics In Banking Complete Deck Ppt Slides

Slide 1: This slide introduces Comprehensive Guide for Implementing Data Analytics in Banking. State your company name and begin.
Slide 2: This is an Agenda slide. State your agendas here.
Slide 3: This slide shows Table of Content for the presentation.
Slide 4: This slide continues showing Table of Content for the presentation.
Slide 5: This slide shows title for topics that are to be covered next in the template.
Slide 6: This slide provides an overview of data analytics aimed at guiding banking institutions to understand its suitability and importance. It covers description, objectives etc.
Slide 7: This slide outlines four reasons why banks need use of data analytics aimed at streamline financial operations and improve customer retention. It covers four arguments.
Slide 8: This slide displays benefits of adopting analytics in banking aimed at utilising these tools and technologies to streamline banking operations.
Slide 9: This slide covers trends of data analytics shaping the banking industry aimed to improve operational costs of the banks in a significant manner. It covers five trends.
Slide 10: This slide shows title for topics that are to be covered next in the template.
Slide 11: This slide showcase a graphical representation of global data analytics usage in banking industry. It includes present and forecasted values of data analytics usage globally.
Slide 12: This slide highlights a graphical representation of market segmentation for data analytics in banking industry. It covers segmentation by region and type of data analytics.
Slide 13: This slide outlines major growth drivers promoting increased use of data analytics in banking market to improve operational efficiency.
Slide 14: This slide displays major growth restraints that may hinder increased use of data analytics in banking market to improve operational efficiency.
Slide 15: This slide shows title for topics that are to be covered next in the template.
Slide 16: This slide provides a summary of data analytics types for discovering trends, patters and correlations to generate data –driven decisions. It covers four types.
Slide 17: This slide shows title for topics that are to be covered next in the template.
Slide 18: This slide outlines the first phase of implementing data analytics into banking industry to streamline operations and enhance efficiency.
Slide 19: This slide displays the first phase of implementing data analytics into banking industry to streamline operations and enhance efficiency.
Slide 20: This slide outlines the second phase of implementing data analytics into banking industry to streamline operations and enhance efficiency.
Slide 21: This slide displays the second phase of implementing data analytics into banking industry to streamline operations and enhance efficiency.
Slide 22: This slide outlines the third phase of implementing data analytics into banking industry to streamline operations and enhance efficiency.
Slide 23: This slide shows title for topics that are to be covered next in the template.
Slide 24: This slide provides an overview highlighting application of data analytics towards major banking domains. It covers three forms of application – risk management, supply etc.
Slide 25: This slide shows title for topics that are to be covered next in the template.
Slide 26: This slide provides an overview of fraud analytics as a medium to detect frauds using data analysis techniques. It covers an overview providing meaning, types of banking frauds etc.
Slide 27: This slide displays an overview of fraud analytics as a medium to detect frauds using data analysis techniques. It covers major techniques and methods used to detect bank frauds.
Slide 28: This slide also provides an overview of fraud analytics as a medium to detect frauds using data analysis techniques.
Slide 29: This slide shows title for topics that are to be covered next in the template.
Slide 30: This slide provides an overview of credit risk analysis as a medium to mitigate risks associated with loans using data analysis techniques.
Slide 31: This slide displays an overview of credit risk analysis as a medium to mitigate loan hazards using data analysis techniques.
Slide 32: This slide shows title for topics that are to be covered next in the template.
Slide 33: This slide provides an overview of sales performance analysis as a medium to increase bank revenue using data analysis techniques. It covers an overview providing meaning etc.
Slide 34: This slide displays an overview of sales performance analysis as a medium to increase bank revenue using data analysis techniques. It covers various business cases.
Slide 35: This slide shows title for topics that are to be covered next in the template.
Slide 36: This slide provides an overview of banking chatbots as a medium to maintain online reputation using data analysis techniques. It covers an overview providing meaning etc.
Slide 37: This slide displays an overview of banking chatbots as a medium to maintain online reputation using data analysis techniques. It covers various stats and trends.
Slide 38: This slide provides an overview of banking chatbots as a medium to maintain online reputation using data analysis techniques. It covers various business cases.
Slide 39: This slide also displays an overview of banking chatbots as a medium to maintain online reputation using data analysis techniques. It covers various business cases.
Slide 40: This slide shows title for topics that are to be covered next in the template.
Slide 41: This slide provides an overview of banking chatbots as a medium to enhance customer service using data analysis techniques.
Slide 42: This slide displays an overview of virtual assistants as a medium to enhance customer service using data analysis techniques. It covers various business cases.
Slide 43: This slide also provides an overview of virtual assistants as a medium to enhance customer service using data analysis techniques.
Slide 44: This slide shows title for topics that are to be covered next in the template.
Slide 45: This slide provides an overview of personalized marketing as a medium to increase customer base using relevant data analysis techniques.
Slide 46: This slide displays an overview highlighting types of personalized marketing used by banks to increase customer base. It covers three types – perceptive, real time and machine learning personalization.
Slide 47: This slide provides an overview of personalized marketing as a medium to enhance demand using data analysis techniques.
Slide 49: This slide shows title for topics that are to be covered next in the template.
Slide 50: This slide provides an overview of recommendation engines as a medium to increase the sale of banking services via relevant data analysis techniques.
Slide 51: This slide outlines steps to implement recommendation engines in the working of banking institutions to boost service usage. It covers four major steps – data collection, data analysis etc.
Slide 52: This slide provides an overview of virtual assistants as a medium to increase the sale of banking services via relevant data analysis techniques.
Slide 53: This slide shows title for topics that are to be covered next in the template.
Slide 54: This slide provides an overview of big data, highlighting large, fast, or complex datasets that are difficult to handle using traditional methods.
Slide 55: This slide outlines major criteria to be followed for implementing analytics for big data and improving its quality. It covers five points such as lack of infrastructure etc.
Slide 56: This slide displays various use cases highlighting use of analytics to segregate and improve quality of big data in banks. It covers cases such as consumer analytics, marketing etc.
Slide 57: This slide outlines various use cases highlighting use of analytics to segregate and improve quality of big data in banks. It covers cases such as consumer analytics etc.
Slide 58: This slide provides a comparative analysis of tools used to analyse and improve quality of big data in banks. The basis of comparison are owner, operational mode, data sources etc.
Slide 59: This slide shows title for topics that are to be covered next in the template.
Slide 60: This slide illustrates a weekly timeline of activities undertaken while implementing data analytics in banking institutions to streamline operations.
Slide 61: This slide shows title for topics that are to be covered next in the template.
Slide 62: This slide highlights team roles under analytics department aimed at understanding their functioning to achieve banking goals and objectives.
Slide 63: This slide also highlights team roles under analytics department aimed at understanding their functioning to achieve banking goals and objectives.
Slide 64: This slide outlines training plan aimed at helping employees learn the basics of data analytics to streamline its usage in the banking institution.
Slide 65: This slide shows title for topics that are to be covered next in the template.
Slide 66: This slide illustrates budget allocation towards analytics tools and activities in form of a pie chart aimed at identifying and altering cost arrangements.
Slide 67: This slide shows title for topics that are to be covered next in the template.
Slide 68: This slide outlines metrics to track performance of analytics aimed at adopting strategies of careful planning and calculations to help banks operate effectively.
Slide 69: This slide showcases impact analysis of above mentioned data analytics strategies on banks via use of major performance indicators. It provides information about NPL ratio etc.
Slide 70: This slide shows title for topics that are to be covered next in the template.
Slide 71: This slide illustrates a dashboard to monitor deposit and account management aimed at evaluating performance to make necessary improvements.
Slide 72: This slide displays a dashboard to monitor loans raised aimed at evaluating operational performance to make necessary improvements.
Slide 73: This slide shows title for topics that are to be covered next in the template.
Slide 74: This slide outlines major challenges faced pertaining to data analytics when implemented in the banking institutions. It covers four challenges – legacy systems, unstructured data etc.
Slide 75: This slide highlights limitations faced while implementing data analytics in banking institutions. It covers five key limitations such as poor quality data, fear of failure etc.
Slide 76: This slide shows title for topics that are to be covered next in the template.
Slide 77: This slide represents use case analysis for Danske Bank utilizing analytics to boost operations efficiently. It provides details about challenges faced, program goals etc.
Slide 78: This slide presents use case analysis United Overseas Bank Singapore utilizing analytics to boost operations efficiently. It provides details about challenges faced, program goals etc.
Slide 79: This slide represents use case analysis for JP Morgan Chase utilizing analytic tool Hadoop to boost operations efficiently.
Slide 80: This slide shows all the icons included in the presentation.
Slide 81: This is a Thank You slide with address, contact numbers and email address along with socials.