Revolutionizing Finance Industry With Machine Learning ML CD

Slide 1: The slide displays Revolutionizing Finance Industry with Machine Learning. State your Company name and begin.
Slide 2: This is an Agenda slide. State your agendas here.
Slide 3: The slide renders Table of contents for the presentation.
Slide 4: The slide continues Table of contents.
Slide 5: This slide highlights key issues faced in the finance department, such as fraud vulnerability, uncertain risk evaluation, customer dissatisfaction, etc.
Slide 6: This slide covers applications and examples of major tech trends in the finance sector, such as decentralized finance (DeFi), machine learning (ML), etc.
Slide 7: The slide represents Title of contents.
Slide 8: This slide gives brief overview of implementing ML in finance industry.
Slide 9: This slide covers popular machine learning algorithms used in the finance industry according to learning types.
Slide 10: This slide contains elements to be considered when implementing machine learning in the finance industry.
Slide 11: The slide highlights Title of contents further.
Slide 12: This slide covers major use cases of machine learning in the finance sector.
Slide 13: The slide displays Title of contents which is to be discussed further.
Slide 14: This slide contains a brief introduction to fraud detection using ML.
Slide 15: This slide covers key areas for implementing ML for fraud detection, such as credit card fraud, email phishing, insurance claims, mobile fraud, etc.
Slide 16: This slide highlights a comparison of rule-based and machine learning for fraud detection.
Slide 17: This slide covers steps for fraud detection using ML such as data collection and labeling, create features, training data and algorithm input, etc.
Slide 18: This slide displays major financial corporates implementing ML such as Compliance.ai, PayPal, MasterCard, Feedzai.
Slide 19: The slide renders Title of contents which is to be discussed further.
Slide 20: This slide gives a brief introduction to ML-based Robo-advisors for automated online investment services.
Slide 21: This slide covers the key applications of ML in Robo-Advisor.
Slide 22: This slide contains the functional process of Robo-advisor.
Slide 23: This slide highlights the comparative analysis of enterprises that use Robo-advisor services.
Slide 24: This slide describes the progression of Robo advisory in the financial sector.
Slide 25: The slide displays Title of contents further.
Slide 26: This slide covers a brief introduction to algo training using ML.
Slide 27: This slide highlights major areas of implementing machine learning in trading.
Slide 28: This slide covers the steps of implementing ML for Algo training.
Slide 29: This slide represents various enterprises using ML for training, such as Morgan Stanley, Numerai, Tino IQ, and Kavout.
Slide 30: The slide presents Title of contents further.
Slide 31: This slide gives brief introduction to stock market investment predictions.
Slide 32: This slide depicts the architecture for using ML for investment predictions.
Slide 33: This slide covers the process of using machine learning for investment prediction.
Slide 34: This slide contains major corporations implementing machine learning for investment predictions.
Slide 35: This slide covers key issues faced while implementing machine learning for investment prediction.
Slide 36: The slide presents Title of contents which is to be discussed further.
Slide 37: This slide depicts the credit scoring process flow after ML implementation.
Slide 38: This slide covers the major components of ML in credit scoring, such as diverse data sources, personalization and precision, etc.
Slide 39: This slide shows the steps of the credit rating procedure, such as data preparation, feature selection, model training and validation, etc.
Slide 40: This slide covers the key enterprises implementing machine learning for credit scoring.
Slide 41: The slide again shows Title of contents.
Slide 42: This slide highlights the automated reconciliation workflow.
Slide 43: This slide covers major automated processes post-machine learning implementation.
Slide 44: This slide again displays major automated processes post-machine learning implementation.
Slide 45: The slide depicts Title of contents further.
Slide 46: This slide covers the workflow of implementing ML for risk management.
Slide 47: This slide displays future scope of implementing ML for financial risk management.
Slide 48: The slide highlights Title of contents which is to be discussed further.
Slide 49: This slide covers major use cases of machine learning in the finance sector.
Slide 50: The slide represents Title of contents further.
Slide 51: This slide covers major improvements in the finance sector post-ml implementation.
Slide 52: The slide renders Title of contents further.
Slide 53: This slide shows key issues of implementing machine learning in the finance sector, such as data quality, data availability, privacy and security, etc.
Slide 54: The slide depicts Title of contents further.
Slide 55: This slide covers future scope of implementing machine learning in finance sector.
Slide 56: This slide shows all the icons included in the presentation.
Slide 57: This slide is titled as Additional Slides for moving forward.
Slide 58: The slide displays Comparative analysis of financial advisor options.
Slide 59: The slide renders Problem definition.
Slide 60: This slide covers major financial institutions for deploying ML such as JP Morgan Chase, 5point credit union, and Danske bank.
Slide 61: The slide displays Jobs titles in machine learning in finance with salaries.
Slide 62: The sldie shows Machine Learning emerging as most impactful trend for FinTech sector.
Slide 63: The slide represents Machine Learning workflow in finance sector.
Slide 64: The slide displays Impact of implementing ML chatbots in finance sector.
Slide 65: This is our vision, mission & goal slide. State your firm goals here.
Slide 66: This is Our Team slide with names and designation.
Slide 67: This slide depicts Venn diagram with text boxes.
Slide 68: This slide provides 30 60 90 Days Plan with text boxes.
Slide 69: This slide shows Post It Notes. Post your important notes here.
Slide 70: This slide shows SWOT describing- Strength, Weakness, Opportunity, and Threat.
Slide 71: This slide displays Column chart with two products comparison.
Slide 72: This is a Thank You slide with address, contact numbers and email address.