February 10, 2020
What affects your success more: your background or your hard work? This is what inspired J.P. Morgan and Bloomberg to host the first ever joint Data for Good Exchange (DFGX) event in London.
Bloomberg has hosted this event for the past six years, examining different issues where corporations, policy makers and researchers can collaborate on data-related projects that can bring us closer to solving them. This year, the company invited J.P. Morgan to co-host the event at Bloomberg’s offices, where attendees from government, academia, businesses and nonprofits gathered to discuss how data science can help address social inequality and mobility.
As a data pioneer, Bloomberg handles 100 billion market data requests each day and has built out a global infrastructure supported by more than 5,000 technologists. For its part, J.P. Morgan has 320 petabytes of stored data — or approximately 640,000 laptops’ worth — and employs 53,000 technologists.
“We have two internationally recognized brands in financial services who truly believe that the use of data and technology really can significantly enhance the work we do in social change,” explained Samik Chandarana, Head of Corporate & Investment Bank (CIB) Data & Analytics and Applied AI & ML at J.P. Morgan. “Public policy and social programs should always be based on good and fulsome data. Data science and its associated practices is a key tool to analyze data. By bringing technical practitioners together with organizations that are operating to affect the social landscape, we can accelerate this change.”
One example is the JPMorgan Chase Institute, the organization that publishes original research to help policymakers, businesses and nonprofit leaders make more informed decisions by using their analysis of the firm’s data to help answer important questions about the financial health of U.S. consumers and businesses. is another initiative that lifts up communities. Through one of its programs, the firm connects technologists with hundreds of nonprofits each year to help build solutions that allow the organizations to further succeed. Bloomberg is also no stranger to corporate responsibility. One of their initiatives includes Teach First, a program dedicated to ending education inequality by advancing innovation and leadership in the education space.
Data science and AI aren’t just used at financial and technology companies — these tools are integral for measuring success of initiatives and predicting outcomes by government officials, social scientists and nonprofits, whether they be dashboards, models or algorithms. This is the reason the partnership between financial institutions and these public sectors are so important. While the nonprofits bring their expertise across nuanced issues, such as financial safety and low social mobility, J.P. Morgan and Bloomberg can provide their advanced resources in data science to build the right tools that can help solve these complex problems.
The discussion featured a panel comprised of Manuela Veloso, Head of AI Research at J.P. Morgan, Lee Elliot Major OBE, Professor of Social Mobility University of Exeter, Guy Rigden, CEO of MyBnk and Gideon Mann, Head of Data Science in Bloomberg’s Office of the CTO.
Panelists also tackled opposing questions including:
“Humans are producing more data every day and computers have the potential to look at that and develop AI algorithms. And so computers need to be able to bring benefits to humanity,” stated Veloso. The partnership between J.P. Morgan and Bloomberg hopes to spark more conversations to do just that. Since the two have presence across the globe, they hope to both impact the world and inspire other partnerships to develop and further advance the initiative in the future.
Watch the full recap to learn more about the event here:
BRG – JPMPB – Bloomberg DFGX London – Transcript
Samik: J.P. Morgan and Bloomberg are incredible partners across the globe. We’re two huge international firms that share very similar views on our corporate and social responsibility. Also, the footprints that we both have across the globe means that we have an ability to have an impact together.
Gideon: Tonight was a unique moment where we all kind of came together and had the opportunity to all focus on the problem of social mobility. My hope is that tonight is one night among many where we get to exchange ideas and build relationships to solve these problems together.
Lee Elliot: Social mobility is about helping young people particularly make good choices in their lives. So your background shouldn’t determine what you choose to do. We know that such groups is low in countries like the U.K. and the U.S. and that means your background determines what you do in life more than whether you work hard or your talent. So we know that we’re missing out on lots of talent in our populations. So I think nations are increasing looking there not just as a social issue, but an economic one as well.
Gideon (NAT): Recently, there have been so many questions about how AI and machine learning should be used and could be used. We always felt as that actually there is not enough AI, not enough machine learning. And in particular there’s not enough AI and machine learning for purposes of enriching our society.
Gideon: Bloomberg’s Immersion Fellow Program takes graduate students in some of their early years and embeds them into nonprofits for a few days. And in those few days, they make a sustentative impact into the nonprofits by telling them how they can apply data science. It’s been an incredibly good model for how to engage nonprofits and the academic sector. We hope to bring it across the world soon.
Another model that has been very effective in bringing nonprofits and data science together has been the workshop program that we have through the Data for Good exchange, where we have people come together and work on a particular problem in the context of this workshop.
Guy: With the program that J.P. Morgan supports, the Money House, it’s aimed at young care leavers, those that are just about to leave local authority care. So by definition, they don’t have the family network and they’re going out to live on their own. We see financial capability as underpinning wellbeing and that underpins the opportunities to advance in social mobility. So we do that by the use of data, by the use of evaluation, and frankly by working with the young people themselves and following up with their lives to see if it’s made a difference.
Guy (NAT): There is a holistic approach to social mobility, but money is central. And I would say to those organizations that wanna help: please concentrate on interventions that have evidence of impact.
Manuela (NAT): What we should all be engaged on is to transform all our experience, all the people we know, all those cases into the language of the computers. And then engage people that know about computing to analyze these.
Manuela: Humans are producing more data every day. And computers have the potential to look at that data. Symbolic data, behavior data, text data, image data, it’s to develop AI algorithms. And so computers need to be able to bring benefits to humanity.
Lee: The thing about data that excites me is that we could develop a more personalized approach to how we support young people. So at the moment, we’ve got general programs that seem to work for some children. I think what data could provide—more data could provide for us is better ways to know who to help and then what to do with them. I think that would be amazing, it would be possibly revolutionary, actually, in terms of social mobility.
Samik: The panel, I though, was incredible because it covered a vast array of topics with also a really different set of opinions. But we’ve gotta be realistic. We need to continue investing, we need to hold many more events like this, we need to start showing people more practical examples of how this can affect them. And then make sure people come on that journey with us. Through programs like this, it can open up massive advantages, but also turn education into a key of the future.