AI in Finance & RegTech Virtual Summit AI in Finance & RegTech Virtual Summit schedule

Fund2Vec: Mutual Funds Similarity Using Graph Learning

Identifying similar mutual funds (including exchange-traded funds) with respect to the underlying portfolios has found many applications in fund recommender systems, competitors analysis, marketing and sales of the products. The traditional methods are either qualitative, and hence prune to biases and often not reproducible,or, are known not to capture all the nuances (non-linearities) among the portfolios from the raw data. We propose a radically new approach based on the weighted bipartite network representation of funds and their underlying assets data using a sophisticated machine learning method called Node2Vec to learn an embedded low-dimensional representation of the network. We use this network embedding to identify similar portfolios by computing node similarities in the representation space, which we call Fund2Vec. Our approach provides novel insights to the portfolio similarity problem as well as a data-driven method to remove bias from qualitative categorizations available in the market. Ours is also the first ever study of the weighted bipartite network representation of the funds-assets network.

Key Takeaways: 1) Machine Learning for mutual funds analytics (sales, marketing, portfolio diversification, etc.) 2) An interesting usecase for application of graph machine learning in investment management industry 3) A bit technical details on a specific graph machine learning algorithm called Node2Vec

Dr Dhagash Mehta is a Senior Investment Strategies Manager (Machine Learning and Asset Allocation) at Investment Strategies Group at Vanguard, and prior to that was a Principal Research Data Scientist at Vanguard. Dr Mehta is an Editorial Board Member at the Journal of Financial Data Science (https://jfds.pm-research.com/). Dr. Mehta pursued his undergraduate studies in Physics in India, followed by Part III of Mathematical Tripos at the University of Cambridge, and Ph.D. in theoretical particle physics from the University of Adelaide (Australia) as well as Imperial College London (UK). Before joining Vanguard, he was a Senior Research Scientist at United Technologies Research Center (now called Raytheon Technology Research Center), and prior to that a Research Professor at Department of Applied and Computational Mathematics and Statistics at University of Notre Dame. He has held multiple research positions at various research institutes such as Fields Institute in Toronto, Simons Institute for Theory of Computing at Berkeley, the University of Cambridge (UK), Imperial College London (UK), the University of Adelaide (Australia), North Carolina State University (USA), Syracuse University (USA) and National University of Ireland Maynooth (Ireland). Dr. Mehta’s areas of expertise are theory of machine/deep learning, and applications of machine learning in finance.. In particular, he has published 75+ research papers in reputed journals on optimization (convex and nonconvex), computational algebraic geometry, numerical analysis, network science and machine learning to solve various problems arising in financial services and wealth/asset management (and in the past, power systems and control theory; theoretical physics, jet-engines, and smart building systems).

PRESENTATION: Speech Recognition: Comprehensive Insights in Real-World AI for Business Leaders

Learn how to streamline your workload, boost efficiency and automate processes with Dragon Speech Recognition. This live short tutorial will show you how you can overcome challenges such as documentation, collaboration across teams in real time and how to get key business insights that will help you perform better as a business and improve your services to customers.

ROUNDTABLE: AI & Speech Recognition in Financial Services

Economies are responding to many challenges (e.g. Brexit and the Covid-19 pandemic). Businesses need to find ways to remain both competitive and sustainable. As markets and profitability wax and wane under these influences, the financial services sector faces real and present pressures. How can we advance speech recognition in this time, and consequently build better products & provide better services?

Vladimir Teodosiev is a Sales Manager at Nuance delivering value to high-profile private and public organisations. Commercially savvy and with a strong interest in technology, he has extensive experience in creating and delivering value to high-profile private and public organisations. Highly customer-focused and results-driven, he supports businesses in developing best practices that facilitate efficient document creation.

Identifying and Addressing Bias in Machine Learning Models Used in Banking

Banks are increasingly relying on Machine Learning models as decision support systems in various areas such as fraud detection, credit scoring, and optimal order execution. When a model makes a decision on a client application, it is important to ensure that the decision is unbiased and explainable, both from regulatory and moral standpoint. This talk will focus on relevant regulations and some of the ways in which these biases can be identified and addressed.

Key Takeaways: 1. Talk will present the types of applications of ML models where ‘biases’ could influence model decisions 2. Applicable regulations in banking domain 3. Some statistical techniques that will help identify the biases and possible course of actions to address the biases.

Kishore Karra is an Executive Director in the Model Review Group at JP Morgan Chase. In this role, he assesses and mitigates risks posed by Machine Learning models used in different areas of the bank. Kishore holds a Master’s degree in Mathematics from Rutgers University and an MBA from the Indian School of Business.

Transformation of Core Business Function Areas Using Advanced Analytics and AI

Financial institutions have already started to embrace AI as a part of their core business strategy. The AI investments are not only focused on enhancing the customer and risk controls analytics but also to transform and modernize their core business function areas. The AI technologies (Automation, ML, NLP, Deep Learning etc) combined with the unprecedented amount of data are helping to increase the operational efficiencies and reduction in cost while mitigating Risk and Compliance issues by creating timely awareness and preventative measures. This talk will touch upon the challenges to establish the AI framework i.e. how to start a journey from foundational analytics to advanced analytics; processes and methodologies to identify and prioritize the business problems those can be solved and scale quickly across business function areas. It will include the POCs of various types of analytics (Self-serving analytics, ML & NLP) with real examples from function areas such as Technology, Finance, Audit & HR.

Key Takeaways: 1. Modernization of Business Core Functions is must and should be a part of overall corporate strategy 2. Variety of AI technologies are been leveraged to achieve this 3. AI solutions are bringing Gain in operational efficiencies and mitigation in operation and reputational risks.

Nitesh Soni is a Director at Scotiabank where he is leading end-to-end advanced analytics and AI projects for Corporate Functions. His career spans over 15 years across top tier banks, consulting firm and world leading research institutes with a core expertise in Predictive modeling, Machine Learning, AI and Big Data. He is a regular speaker at the various national and international conferences. While as a researcher, he has been a part of a couple of major discovery experiments and has won few prestigious awards. He holds a PhD degree in Experimental Particle Physics.

Transforming Facebook Finance Operations with AI

Facebook’s Enterprise Products Applied Research team develops AI solutions to enhance productivity for our Finance operations. In this talk, we present AI solutions we’ve developed for several stages of the order-to-cash cycle: credit management, collections prioritization and cash application. Managing credit limits for Facebook’s advertisers requires a large and rapidly growing number of credit decisions. We built a machine learning solution to assess advertisers for credit risk and help automate credit decisions. Our model has enabled Facebook to automate the majority of credit decisions while avoiding revenue loss from bad debt write offs as well as from credit outages for good accounts. We also use machine learning to prioritize collection activities. We proactively forecast probability of invoice bad debt write-off, and prioritize collections activities based on expected losses. This ML-based prioritization has led to faster payment. Finally we present an AI-based solution for automating cash application. We use OCR to automatically read remittance instructions, and apply payments accordingly. This automation has led to productivity savings and shorter cash application cycle time.

Key Takeaways, how FB uses AI to; 1) Automate credit decisions 2) Prioritize collection resources 3) Understand business documents

Julie Ward Drew is an Engineering Manager in Facebook’s Enterprise Engineering organization, where she builds AI & machine learning solutions for finance and compliance. She has over 20 years of experience applying optimization and machine learning to enable step-level improvements across a wide range of enterprise applications. Julie was awarded the 2009 Franz Edelman Prize for Outstanding Achievement in the Practice of Operations Research and Management Science by INFORMS. She received a Ph.D. in Operations Research from Stanford University and a Sc.B. in Applied Mathematics from Brown University.

Transforming Facebook Finance Operations with AI

Paul Brandenburg is a Program & Analytics Manager in Facebook’s Finance organization, where he works on leveraging technology & data to better serve new and existing invoicing customers. He has 13 years of experience across strategy consulting (BCG), corporate development (News Corp) and product management (Amazon). He holds an MBA with Distinction from Columbia Business School and a BSc. in International Relations from the London School of Economics.

Transforming Facebook Finance Operations with AI

Facebook’s Enterprise Products Applied Research team develops AI solutions to enhance productivity for our Finance operations. In this talk, we present AI solutions we’ve developed for several stages of the order-to-cash cycle: credit management, collections prioritization and cash application. Managing credit limits for Facebook’s advertisers requires a large and rapidly growing number of credit decisions. We built a machine learning solution to assess advertisers for credit risk and help automate credit decisions. Our model has enabled Facebook to automate the majority of credit decisions while avoiding revenue loss from bad debt write offs as well as from credit outages for good accounts. We also use machine learning to prioritize collection activities. We proactively forecast probability of invoice bad debt write-off, and prioritize collections activities based on expected losses. This ML-based prioritization has led to faster payment. Finally we present an AI-based solution for automating cash application. We use OCR to automatically read remittance instructions, and apply payments accordingly. This automation has led to productivity savings and shorter cash application cycle time.

Key Takeaways, how FB uses AI to; 1) Automate credit decisions 2) Prioritize collection resources 3) Understand business documents

David Chi is an Applied Research Scientist in Facebook's Enterprise Engineering organization. He works on machine learning and artificial intelligence for internal applications used by finance, supply chain, and compliance organizations. He holds a bachelor's degree in engineering from UC Berkeley and a Ph.D. in engineering from Stanford University.

PRESENTATION: How to Explain an ML Prediction to Non-Expert Users?

Machine Learning has provided new business opportunities in the insurance industry, but its adoption is for now limited by the difficulty to explain the rationale behind the prediction provided. In our latest research, we explore how we can enhance one type of explanations we can extracted from interpretability method called local feature importance explanations for non-expert users. We propose design principles to present these explanations to non-expert users and are applying them to a car insurance smart pricing interface. We present preliminary observations collected during a pilot study using an online A/B test to measure objective understanding, perceived understanding and perceived usefulness of our designed explanations.

ROUNDTABLE: Challenges of ML Interpretability and Business Opportunities

The rise of Machine Learning (ML) has provided new business opportunities in the insurance industry. ML can for instance help improve pricing strategies, fraud detection, claim management or the overall customer experience. Yet, its adoption can be for now limited by the difficulty for ML to explain the rationale behind predictions. What can be explained from ML models? What do people need to be explained of? How to present explanations? These are some challenges we want to address in ML Interpretability

I am currently working as a Researcher at AXA and am a PhD Candidate at Laboratoire Informatique de Paris 6 (LIP6). I conduct research on eXplainable AI (XAI) and User Experience (UX) in Machine Learning. I graduated from a Design Master’s Degree in 2015 and works for several years as a User Experience Designer in various fields before starting research on Human+AI interactions.

Xavier Renard is a researcher in data science and machine learning at AXA. He completed a Ph.D. in computer science at Sorbonne University in Paris. His research focuses on the topic of Responsible and Trustworthy AI, in particular on machine learning interpretability.

PRESENTATION: Finance Is Not All About Numbers...What About Words?

Every company, regardless of industry, owns some level of business expertise. Every company, at one point or another, experiences the same challenge: scaling that expertise. As a bank, how do you deliver quality service to your customers when inquiries triple on your digital channel in a matter of months?   How do you scale risk management or your onboarding operations when those teams are already at capacity? The capability to integrate technology that can read and understand unstructured documents is no longer a “nice-to-have” within the financial services industry. It’s a necessity. Learn how Natural Language Understanding can power your business.

Key Takeaways: * Why language must be leveraged as data

• How language can be transformed into actionable knowledge

*How to easily use our Natural Language Understanding technology

ROUNDTABLE: The Value of Language in Finance: Demo & Discussion

Learn how your organization can use Natural Language Understanding based solutions to scale your enterprise. The capability to integrate technology that can read and understand unstructured documents is no longer a “nice-to-have” within the financial services industry. It’s a necessity. Join us to listen to real world examples and see, firsthand, how Natural Language can power your business.

Key discussion topics: Live demonstration which goes beyond data mining. Discover how our AI automatically reads, learns and understands the financial industry knowledge at speed and scale. Learn more about how to transform language into value thanks to concrete KPIs and benefits.Learn how your organization can use Natural Language Understanding based solutions to scale your enterprise. The capability to integrate technology that can read and understand unstructured documents is no longer a “nice-to-have” within the financial services industry. It’s a necessity. Join us to listen to real world examples and see, firsthand, how Natural Language can power your business.

Key discussion topics: *Live demonstration which goes beyond data mining.

*Discover how our AI automatically reads, learns and understands the financial industry knowledge at speed and scale.

*Learn more about how to transform language into value thanks to concrete KPIs and benefits.

Pamela Negosanti is the head of sales and sector strategy for financial services and insurance at expert.ai. She specializes in helping companies execute business transformation through artificial intelligence. Pamela has spent the majority of her career in the technology sector where she has established expertise in artificial intelligence, cognitive computing, intelligent automation, natural language understanding and semantics, to name a few. She owns a degree in translating and interpreting with a specialization in computational linguistics, and speaks fluent Italian, English, French and German. Fueled by change and curiosity, she is firm believer in the power of knowledge share and communities.

PRESENTATION: Emerging Technology Trends in North American Financial Services

Join this presentation to delve into the latest technology trends impacting financial service institutions. Katie will spend 20 mins discussing the status, potential impact and the journey to understanding and piloting these trends.

ROUNDTABLE: Discussing Barriers to Adopting Emerging Technologies

Join Katie to discuss issues and barriers that prevent emerging technology adoption including but not limited to; maturity, change management, risk management, user acceptance, upskilling. What do you see as pressing issues to solve when it comes to implementing technology solutions?

Katie is the Head, Technology Research & Innovation at BMO Financial Group. This mandate sees her seeking out knowledge of emerging technologies and determining their maturity and applicability for Financial Services. She runs a team that undertakes research in house as well as partnering with institutions & organizations that are shaping the future of financial technology.

Trust Me, I’m a Robot – The Future of AI and Automation in Insurance

Technology is changing the way we live and consume service. Insurance has perhaps been the slowest to react to these changes but it is apparent that data is starting to drive personalisation and precision in insurance. “Real-time-risk-management” enabled by the “subscription model” is, in the very near future, going to become main stream in personal insurance. Advancements in data sciences coupled with a fourfold increase in the number of sensors is going to lead to a seismic shift in insurance models from the traditional insurance model of “Protection” to “Prevention”.

Key Takeaways: 1. Data is enabling a shift in the insurance model from protection to prevention.

1. Whilst Automation is going to help reduce processing costs, AI will provide deep insights and the ability to predict.

2. AI will enable insurance costs to be viewed as a “service charge” rather than an “annual tax”

Manan is a highly experienced insurance professional and a pragmatic business leader. He has previously lead Lockton’s Singapore business where he delivered organisation-wide changes and then went on to manage one of the largest acquisitions in the insurance industry. A Chartered Accountant by profession and now a technologist by trait, Manan is well regarded for his thought leadership. His career has spanned across the Americas, EMEA and Australasia. As Fujitsu’s Insurance CTO, Manan is responsible for defining the innovation strategy for the insurance sector. In his role he is a strategic advisor to the insurance sector on digital transformation and connected technology solutions.

How Can Machine Learning Help in Getting the Maximum out of the Marketing Budget or Minimize Marketing Waste

Organizations spend a fortune on marketing. Every dollar spent on marketing demands ROI. With a number of marketing channels in place, be it offline or online - organizations always strive to improve the targeting. Marketing aims to target customers at the right time, with the right product, at optimized price and best time. All of this marketing effort makes it really difficult for the marketing teams to plan and allocate resources and budget. Marketing budgets (particularly after a strong social media presence) have to optimize the budgets and improve the targeting methodology. Data analysis, machine learning and AI allows us to improve customer targeting, product placements. At the same time, these capabilities allow marketing teams to really get the maximum from their budgets. In other words, the teams can minimize the “marketing waste”. This talk focuses on sharing insights on - how can the organizations improve their customer targeting and optimize their marketing budgets.

Key Takeaways 1) What is required to have a successful ML/AI setup for retail 2) What are the areas in the retail industry which can be benefitted by ML/AI 3) What are the common pitfalls to avoid

Vaibhav Verdhan is a seasoned data science professional with rich experience spanning across geographies and domains. He is a published author with books on machine learning and deep learning. He is a hands-on technical expert and has led multiple engagements in Machine Learning and Artificial Intelligence. He is a leading industry expert, is a regular speaker at conferences and meet-ups and mentors students and professionals. Currently he resides in Ireland and is working as a Principal Data Scientist at Johnson and Johnson.

Transformation of Core Business Function Areas Using Advanced Analytics and AI

Financial institutions have already started to embrace AI as a part of their core business strategy. The AI investments are not only focused on enhancing the customer and risk controls analytics but also to transform and modernize their core business function areas. The AI technologies (Automation, ML, NLP, Deep Learning etc) combined with the unprecedented amount of data are helping to increase the operational efficiencies and reduction in cost while mitigating Risk and Compliance issues by creating timely awareness and preventative measures. This talk will touch upon the challenges to establish the AI framework i.e. how to start a journey from foundational analytics to advanced analytics; processes and methodologies to identify and prioritize the business problems those can be solved and scale quickly across business function areas. It will include the POCs of various types of analytics (Self-serving analytics, ML & NLP) with real examples from function areas such as Technology, Finance, Audit & HR.

Key Takeaways: 1. Modernization of Business Core Functions is must and should be a part of overall corporate strategy 2. Variety of AI technologies are been leveraged to achieve this 3. AI solutions are bringing Gain in operational efficiencies and mitigation in operation and reputational risks.

Nitesh Soni is a Director at Scotiabank where he is leading end-to-end advanced analytics and AI projects for Corporate Functions. His career spans over 15 years across top tier banks, consulting firm and world leading research institutes with a core expertise in Predictive modeling, Machine Learning, AI and Big Data. He is a regular speaker at the various national and international conferences. While as a researcher, he has been a part of a couple of major discovery experiments and has won few prestigious awards. He holds a PhD degree in Experimental Particle Physics.

Ronan is a Strategy & Innovation Manager at NatWest, with an academic background examining potential inequality outcomes from widespread “AI” adoption. He has previously helped break ground on AI Model Risk Governance, Emerging Technology Strategy, and Platform Business Models within Financial Services.

Mr. Bryan directs the presales team with MANTA. Prior to joining MANTA from BlackLine, a financial close management SaaS provider where he was Vice President, Global Presales. Over a 20+ year career in the information technology sector, Mr. Bryan has guided technology teams with Computer Associates and Infor. Before focusing on technology, Mr. Bryan served as Chief Financial Officer in the services industry. Mr. Bryan received his Bachelor of Science degree from the University of Virginia.

Mackenzie Wallace is passionate about the use of data and technology, both as a product innovator and financial regulator. He is co-author of the World Bank’s 2021 technical note, “The Next Wave of Suptech Innovation: Suptech Solutions for Market Conduct Supervision,” on the growing use of such solutions by financial authorities globally. He is a former financial regulator and early employee of the U.S. Consumer Financial Protection Bureau (CFPB), where he helped pioneer the authority’s innovative consumer complaint system and public complaint database. He also served as Fintech Policy Advisor at USAID where he helped create the RegTech for Regulators Accelerator (R2A), working with financial authorities globally to embed data and technology into supervision. He currently serves as Director and Head of Product at fintech, MPOWER Financing, designing inclusive financial products to make higher education more accessible.