Machine Learning in Financial Services Virtual Conference
DAY 1|30th June 2021
8:10 am What Role Does Ethics Play in Machine Learning?
- Creating governing bodies to ensure the appropriate use of sufficient sample size populations when making decisions
- Looking towards the future of regulation when the outputs are trusted more allowing more autonomous functions to be used within financial processes
- Ensuring the proper use of consumer data and following data protection and privacy laws
- Creating a Machine Learning pipeline in a safe and governed manor to be able to pass model risk management procedures
Head of Big Data
8:40 am Next Best Action - From “House View” to “Recommended For You”
- What can data tell us about client preferences on ESG, investment styles, investment topics, and more?
- Which AI methodologies are used to create personalised recommendations?
- How to combine portfolio alerts with concrete recommendations for action to increase advisor productivity and client engagement?
- How are data-driven recommendations integrated into regulatory frameworks such as FIDLEG, MiFID 2, PRIIP?
- DEMO: From “Portfolio Alerts” to concrete Next Best Actions and personalized investment proposals
Head of Digital Advisory
9:10 am Debate: Is Machine Learning Mature Enough to Successfully Implement in Financial Institutions
While everyone is quick to jump onto the Machine Learning trend, is it really safe to implement within the financial services sector with so many issues surrounding the regulatory and ethical side of utilizing machines to make human decisions?
- Overcoming the issues faced when explaining outcomes that may be discriminatory which can damage a company’s reputation
- Is Machine Learning really needed to automate financial processes or does the negativity around ethical considerations enough to reconsider?
- Can regulatory bodies ever be confident enough in the decisions made by the machines to allow ML to really progress in financial services?
- Looking towards ensuring transparency in the models decision making process to determine if it is suitable for deployment in financial decisions
Chief Technology Advocate
9:40 am Cleaning Data for ML and Fintech: Real World Lessons from the Plumber
Even the most advanced ML, AI and BI systems are only as good as their inputs. We’ll look at lessons from actual data projects that can apply to anyone building a training corpus or working with production datasets from different sources
- Issues with names and identifiers
- Conflicting taxonomies
- Considering context when enriching data
- Preserving the provenance of data elements
- Aggregating and comparing: apples-to-apples, apples-to-oranges or apples-to-bicycles?
10:10 am Case Study: Navigating the Challenges Presented with Regulation Changes in Machine Learning Such as Those Seen in the Internal Ratings Based (IRB) Review
- Ensuring governance compliance with new machine learning models through IRB repair
- Learning to associate with new algorithms in order to comply with new policy
- Efficiently incorporating new default definitions into machine learning algorithms
- Working through issues with antiquated data when going through validation processes from governing agencies
Assistant Vice President- Predictive Modelling
10:40 am Using observational AI to enhance data quality
Join Stephan Dietrich, AI Expert and Senior Data Scientist from SmartStream’s dedicated Innovation Lab, who will be sharing insight on how AI and machine learning can easily identify relationships between structured and unstructured data; beyond those that can be discovered by human resources.
You will find out more about:
- Operational management and data quality processes
- Transforming complex data processes with AI and observational learning
- SmartStream’s dedicated innovations department – the Innovations Lab
AI Expert and Senior Data Scientist
11:00 am Break
11:10 am How Shared Data (Federated Learning) Can Ensure Smarter Decisions are Being Made Based on a Wider Range of Data in Machine Learning
- Does the idea of shared data breach certain data protection laws such as GDPR?
- Exploring effective ways of navigating the issue of keeping data within national borders while still being able to access the benefits of Shared Data
- Allowing cases of fraud to be picked up on more accurately due to the increase in data
- Looking at the P27 Nordic platform as an example of how Federated Learning can benefit both the financial institution and customer
11:40 am Building An AI & ML Strategy for the Future of FinServ Companies
Unstructured data sources like customer feedback and interactions constitute up to 80% of the data in your company. Join us to discuss how FinServ companies should leverage unstructured data in combination with structured data, and the opportunities at hand for teams that can strategically apply ML, AI, and automation.
- How can ML and AI work together to tackle the challenge of turning unstructured data into valuable insights?
- Use cases for ML & AI applied together to tackle issues like risk/compliance and complaints across multiple customer touchpoints
- What are the strategic capabilities and skillsets necessary to include unstructured data in your short and long-term ML strategy?
- Insights into the unique opportunities in unstructured customer data from the former head of USAA’s Voice of the Customer program
Senior Vice President of CX Strategy and Analytics
12:10 pm How Deploying Machine Learning in Customer Functions can Increase Satisfaction and Customer Loyalty
- Using the outputs from the machines that allow businesses to offer tailor chosen products for each costumer
- Offering the privacy of choosing new products to purchase without needing an agent based on ML recommendations
- Looking to the inclusion of the automated chatbox to offer quick customer resolutions outside of business hours
- Allowing access to services to those who may not have been approved without the data used in machine learning algorithms
Angela Johnson De Wet
Head of Tech Change Risk
Lloyds Banking Group
12:40 pm How Machine Learning Augments Loan Offers Using Propensity-to-Buy Modelling
- How to augment banking campaign mng. tool to better target clients based on a strong, predictive model
- Move from client segmentation to more granular “social networks”
- Find behavioural similarities among customers (beyond traditional demographics), combined with more complex usage/patterns in customer transactional data
Business Dvlp. Manager, Big Data & Data Science
1:10 pm Panel Discussion: How to Navigate Data That Has Been Skewed Due to The Covid-19 Pandemic
- Understanding the impact Covid-19 has had on data sets that could in turn influence outcomes decided by machines
- Will the data go back to the way it was pre-pandemic or is this new customer behavior the new norm?
- What categories have been affected the most by the change in data post-Covid
- Providing a thorough frame work to ensure the changing data is considered accurately when making decisions
Head of Data Science & Analytics
Chief Data Officer
1:30 pm Lunch Break
2:00 pm Navigating the Common Data Issues in Implementing Machine Learning into Financial Services
- Understanding the historical trends that may affect the validity of the data such as the recent pandemic
- Having an infrastructure that can adapt to work through new data and algorithms in shorter time frames to keep up with regulatory standards
- Ensuring the outputs are thoroughly checked to circumvent any instances of bias decision making
- Making sure the design of the machine learning platforms are accurate to process large numbers of data
Fraud Machine Learning Manager
2:30 pm How to Bring the C-Suite on Board for your AI Projects
- Build a Data science culture
- Ask the right questions
- Connect to the community
- Technology considerations
- Select the right line of business to partner with
- Trust in AI
EMEA Head of Artificial Intelligence
3:00 pm Soft-Launch Automation for Machine Learning Models
- low-cost high-fidelity approach for testing ML models before full model production
- keys steps for a soft-launch automation in the financial services context
- bid-tape optimization: case study for soft-launch
Head of ML & Data Science
3:30 pm Break
3:40 pm Ensuring Social Awareness when Using Legacy Data in Machine Learning Processes
- Ensuring decisions made with Machine Learning systems are not discriminatory based on age, sex or race
- Establish the ability to explain the reasoning certain outcomes were reached using the algorithms
- Considering a third party review to ensure that there are no instances of discrimination in the data outputs
- Exploring the regulation and governance over the ethical issues presented when using machine learning and how to stay compliant
Director Service Optimization, Transformation
4:10 pm How Citizens Bank Digitally Transformed Credit Card Fraud and Claims Processes
Financial Institutions using traditional legacy Credit Card fraud & claims processes are paying a price, and the costs aren’t just financial— they include opportunity costs, labor costs, and reputational damage, too. Bottom line: Banks who aren’t using the latest technologies to improve the way they handle customer claims are at a disadvantage.
In an era where customer experience can make or break your financial institution, banks have to optimize and automate or risk getting left behind.
You will walk away learning:
- Citizens Bank re-imagined their Credit Card fraud and claims processes
- What building blocks made up their old system and how they transformed
- How core technologies were leveraged to reduce errors, speed up resolution time, and create better experiences for customers.
Senior Vice President
Director of Professional Services
Senior Director, Banking & Financial Services
4:40 pm Implementing Machine Learning to Help Financial Institutions Serve the Underserved Communities
- Branching away from only Prime and Near Prime customers to allow access to the Sub Prime customers and tap into an underserved market
- Bridging the cultural gap in organizations by being more inclusive due to smarter machine learning decisions
- Looking towards new innovations to allow easier access to those in underserved communities to manage their finances and climb the financial ladder
- Creating a level of trust in financial institutions for subprime communities thus creating more businesses for banks
5:10 pm Utilizing Machine Learning as a Tool to Portray Signals in Investment Banking
- Understanding how to utilize the outcomes from several data points to make decisions in investment
- Looking to the future of regulation as machine learning becomes more refined and sophisticated
- Explaining decisions made when utilizing the signals pointed out by the machines
- Machine Learning as a semi-autonomous function to support human tasks as a means to prevent errors such as the mini crash of 2008
Bank of America Merrill Lynch
5:40 pm Looking to the Emerging Markets for the Future of Machine Learning Innovation
- Due to the impact of Covid the emerging markets in China, Singapore and India have taken major strides in their commitment to advancing their ML processes
- Exploring the advances in the payment systems in countries such as India utilizing Machine Learning functions to streamline efficiency
- Looking towards the alternative lending platforms available in emerging markets to allow access to underserved communities
- Assessing the impact on workers in industries whose roles may be automated thus exacerbating socio-economic issues within vulnerable populations
6:10 pm Chair’s summary and close of conference
To enquire about sponsorship opportunities for the conference, please contact:
Head of Sponsorship
T: +44 (0) 20 7936 6552
The Machine Learning in Financial Services programme is written in collaboration with industry, if you have a case study, idea or just a comment, please contact:
T: 0161 359 5938