Data is the foundation for today’s business decisions. Yet, communicating that data inpresentations can overwhelm your audience. Reena Kansal of Leadership Story Lab explainshow to use the art of storytelling to make your message more engaging – and memorable.Show Notes:Do you need to communicate data and other complex ideas to your audience? Businessstorytelling is all about how you persuade your audience to take action. Join Reena Kansal ofLeadership Story Lab explains how to use the art of storytelling to engage your audience andhelp them remember what is most important.Skip ahead to these highlights:1:33 About Leadership Story Lab3:43 Make your audience remember you7:12 Difference between proving and persuading9:50 Let the Story Do the Work11:20 Create meaning for your audience12:30 The forgetting curve – what do you want your audience to remember?18:00 Executive summary vs executive story18:50 Recommendations for becoming a better storyteller20:00 Become a story collector21:46 Story helps you be known on a deeper level25:20 Example of a well-crafted storyGet more information at LeadershipStoryLab.com

Data is the foundation for today’s business decisions. Yet, communicating that data in presentations can overwhelm your audience. Reena Kansal of Leadership Story Lab explains how to use the art of storytelling to make your message more engaging – and memorable.Show Notes: Do you need to communicate data and other complex ideas to your audience? Business storytelling is all about how you persuade your audience to take action. Join Reena Kansal of Leadership Story Lab explains how to use the art of storytelling to engage your audience and help them remember what is most important. Skip ahead to these highlights: 1:33 About Leadership Story Lab 3:43 Make your audience remember you 7:12 Difference between proving and persuading 9:50 Let the Story Do the Work 11:20 Create meaning for your audience 12:30 The forgetting curve – what do you want your audience to remember? 18:00 Executive summary vs executive story 18:50 Recommendations for becoming a better storyteller 20:00 Become a story collector 21:46 Story helps you be known on a deeper level 25:20 Example of a well-crafted story Get more information at <a href="http://LeadershipStoryLab.com" rel="nofollow" target="_blank">LeadershipStoryLab.com</a>

Become a Better Business Storyteller 

Your data-driven presentation is not just about data. At least 50% of your presentation is abouthow you package and present the data, says Dr. Bill Franks. He leads the Center for Statisticsand Analytical Research at Kennesaw State University in Georgia, and is author of Winning theRoom -- Creating and Delivering an Effective Data Driven Presentation. Listen to this episode toget practical tips for making your next presentation more effective.Jump ahead to these topics:1:17 – About Dr. Bill Franks2:36 – It’s not just about the facts and figures4:40 – Match your content to your audience6:50 – It’s not about you, it’s about what your audience needs9:20 – Analogies are effective for nontechnical people11:30 – Use the fewest numbers possible, at the lowest level of precision13:30 – How effective are diagrams?17:30 – Position your results as positively as possible19:18 – Engaging your Zoom audienceLearn more about Dr. Bill Franks at bill-franks.com. And look for his book, Winning the Room,on Amazon.

Your data-driven presentation is not just about data. At least 50% of your presentation is about how you package and present the data, says Dr. Bill Franks. He leads the Center for Statistics and Analytical Research at Kennesaw State University in Georgia, and is author of Winning the Room -- Creating and Delivering an Effective Data Driven Presentation. Listen to this episode to get practical tips for making your next presentation more effective.Jump ahead to these topics:1:17 – About Dr. Bill Franks 2:36 – It’s not just about the facts and figures 4:40 – Match your content to your audience 6:50 – It’s not about you, it’s about what your audience needs 9:20 – Analogies are effective for nontechnical people 11:30 – Use the fewest numbers possible, at the lowest level of precision 13:30 – How effective are diagrams? 17:30 – Position your results as positively as possible 19:18 – Engaging your Zoom audienceLearn more about Dr. Bill Franks at <a href="http://bill-franks.com" rel="nofollow" target="_blank">bill-franks.com</a>. And look for his book, Winning the Room, on Amazon.

How to Deliver a Data-Driven Presentation

Are cryptocurrencies going mainstream? And what should you know before you accept cryptocurrency as payment? Rachel Cannon, partner at Steptoe and Johnson LLP, reveals the truths of cryptocurrency and what you need to know now.Skip ahead to these topics::49 – About Rachel Cannon1:20 – How Rachel got interested in cryptocurrency3:00 – What is cryptocurrency and how did it start?5:30 – Who is engaging with cryptocurrency and why?7:00 – What should people consider before accepting cryptocurrency?9:33 – They myths surrounding cryptocurrency12:45 – Example of Russian community that leveraged cryptocurrency to bypass their local banking system15:25 – The merits of blockchain and cryptocurrency18:50 – The volatility of cryptocurrency22:00 – What are NFT’s?26:25 – Why brands are using NFT’s27:00 – Why artists love the blockchain28:10 – What does cryptocurrency regulation look like?33:30 – What is the future of cryptocurrency?35:05 – Sources to learn more about cryptocurrencyConnect with Rachel at rcannon@steptoe.comTo learn more about Data Dialogues: https://www.equifax.com/business/trends-insights/data-dialogues-podcast/

Are cryptocurrencies going mainstream? And what should you know before you accept cryptocurrency as payment? Rachel Cannon, partner at Steptoe and Johnson LLP, reveals the truths of cryptocurrency and what you need to know now. Skip ahead to these topics: :49 – About Rachel Cannon1:20 – How Rachel got interested in cryptocurrency3:00 – What is cryptocurrency and how did it start?5:30 – Who is engaging with cryptocurrency and why?7:00 – What should people consider before accepting cryptocurrency?9:33 – They myths surrounding cryptocurrency12:45 – Example of Russian community that leveraged cryptocurrency to bypass their local banking system15:25 – The merits of blockchain and cryptocurrency18:50 – The volatility of cryptocurrency22:00 – What are NFT’s?26:25 – Why brands are using NFT’s27:00 – Why artists love the blockchain28:10 – What does cryptocurrency regulation look like?33:30 – What is the future of cryptocurrency?35:05 – Sources to learn more about cryptocurrency Connect with Rachel at <a href="mailto:rcannon@steptoe.com">rcannon@steptoe.com</a>To learn more about Data Dialogues: <a href="https://www.equifax.com/business/trends-insights/data-dialogues-podcast/" rel="nofollow" target="_blank">https://www.equifax.com/business/trends-insights/data-dialogues-podcast/</a>

Cryptocurrency: What you Need to Know

As we kick off 2022, we’re seeing the convergence of economic headlines and the energy market. How are inflation and other economic trends in the energy market impacting the consumer and commercial sectors? Mark Zoff, manager of market analytics at AEP Energy explains.Jump ahead to these topics::50 - About Mark Zoff and AEP Energy2:00 - Inflation now and what is transitory inflation?4:50 - Economic trends in the energy market and implications for 20229:00 - The 1970’s economy as historical precedent 13:30 - Other historical precedents16:45 - Mark’s thoughts on the PPP protection and its impact on small business19:00 - PPP demonstrates importance of small business sector20:30 - Which data points Mark and the Fed are reserve is focusing on right nowConnect with Mark: mszoff@aepenergy.comTo learn more about Data Dialogues: https://www.equifax.com/business/trends-insights/data-dialogues-podcast/

As we kick off 2022, we’re seeing the convergence of economic headlines and the energy market. How are inflation and other economic trends in the energy market impacting the consumer and commercial sectors? Mark Zoff, manager of market analytics at AEP Energy explains. Jump ahead to these topics: :50 - About Mark Zoff and AEP Energy2:00 - Inflation now and what is transitory inflation?4:50 - Economic trends in the energy market and implications for 20229:00 - The 1970’s economy as historical precedent 13:30 - Other historical precedents16:45 - Mark’s thoughts on the PPP protection and its impact on small business19:00 - PPP demonstrates importance of small business sector20:30 - Which data points Mark and the Fed are reserve is focusing on right now Connect with Mark: <a href="mailto:mszoff@aepenergy.com">mszoff@aepenergy.com</a> To learn more about Data Dialogues: <a href="https://www.equifax.com/business/trends-insights/data-dialogues-podcast/" rel="nofollow" target="_blank">https://www.equifax.com/business/trends-insights/data-dialogues-podcast/</a>

When Inflation and the Energy Market Collide

Dr. Nikhil Paradkar, assistant professor in finance at the Terry College of Business at the University of Georgia, discusses his research on using data and machine learning to better understand how financial changes due to regulation, technological advancements or crises can impact the availability of credit for households. He also tells our host, Jeff Dugger, Principal Data Scientist and University Research Director at Equifax, about some very interesting research on corporate buzzwords, innovation and company earnings calls.Jump ahead to these topics::58 - Paradkar provides an overview of his work at UGA1:35 - Paradkar explains the research he presented to the Consumer Financial Protection Bureau on bank funding shocks3:35 - If a consumer’s credit limit is reduced, how does it impact their credit score?5:00 - Can consumers who are more exposed to their bank’s liquidity shocks have an impact on a financial recovery?6:40 - The CFPB’s reaction to Paradkar’s research9:10 - What machine learning has revealed about consumer finance and Fintechs12:25 - Paradkar explains his machine learning technique used in his research13:38 - Can lenders use Paradkar’s research to improve their lending?15:02 - Is there a latent unobservable variable that causes FinTech borrowers to be more likely to default?16:41 - How Paradkar uses machine learning to study corporate buzzwords, innovations and quarterly earnings callsLearn more about our guest, Nikhil Paradkar: https://www.terry.uga.edu/directory/finance/nikhil-paradkar.html

Dr. Nikhil Paradkar, assistant professor in finance at the Terry College of Business at the University of Georgia, discusses his research on using data and machine learning to better understand how financial changes due to regulation, technological advancements or crises can impact the availability of credit for households. He also tells our host, Jeff Dugger, Principal Data Scientist and University Research Director at Equifax, about some very interesting research on corporate buzzwords, innovation and company earnings calls. Jump ahead to these topics: :58 - Paradkar provides an overview of his work at UGA1:35 - Paradkar explains the research he presented to the Consumer Financial Protection Bureau on bank funding shocks3:35 - If a consumer’s credit limit is reduced, how does it impact their credit score?5:00 - Can consumers who are more exposed to their bank’s liquidity shocks have an impact on a financial recovery?6:40 - The CFPB’s reaction to Paradkar’s research9:10 - What machine learning has revealed about consumer finance and Fintechs12:25 - Paradkar explains his machine learning technique used in his research13:38 - Can lenders use Paradkar’s research to improve their lending?15:02 - Is there a latent unobservable variable that causes FinTech borrowers to be more likely to default?16:41 - How Paradkar uses machine learning to study corporate buzzwords, innovations and quarterly earnings calls Learn more about our guest, Nikhil Paradkar: <a href="https://www.terry.uga.edu/directory/finance/nikhil-paradkar.html" rel="nofollow" target="_blank">https://www.terry.uga.edu/directory/finance/nikhil-paradkar.html</a>

UGA: What Data Reveals About Consumer Finance 

The auto industry has been front and center as supply chain issues make headlines. But the other story is rapid change. Evolving consumer preferences, shifting demographics and digital retailing have all upended the market since COVID. Have these factors changed the industry for good? Equifax’s Rissa Reddan gets a market assessment from Tyson Jominy, vice president of data and analytics at JD Power.Jump ahead to these highlights::50 - Tyson’s role at JD Power1:45 - What data is revealing about the auto industry right now2:53 - Challenges facing the industry and Covid’s role in current environment4:24 - Covid’s impact on individual transit: types of leases, purchases and specific behavioral change5:45 - Electric vehicle market is heating up7:00 - Are we at the tipping point for EV’s?8:10 - Convergence of solar power and EV’s9:00 - How is data driving the digital retail revolution?10:42 - What are we learning about consumers through digital retailing? What does the data reveal?12:15 - New generational trends that are emerging14:12 - How can the auto industry use data to drive recovery? What benefits are we seeing?16:15 - What are cars telling us -- and are they watching their drivers?18:12 - Car data can be a risk assessment19:11 - How this is a time of transition for the auto market and what the future looks like

The auto industry has been front and center as supply chain issues make headlines. But the other story is rapid change. Evolving consumer preferences, shifting demographics and digital retailing have all upended the market since COVID. Have these factors changed the industry for good? Equifax’s Rissa Reddan gets a market assessment from Tyson Jominy, vice president of data and analytics at JD Power.Jump ahead to these highlights::50 - Tyson’s role at JD Power1:45 - What data is revealing about the auto industry right now2:53 - Challenges facing the industry and Covid’s role in current environment4:24 - Covid’s impact on individual transit: types of leases, purchases and specific behavioral change5:45 - Electric vehicle market is heating up7:00 - Are we at the tipping point for EV’s?8:10 - Convergence of solar power and EV’s9:00 - How is data driving the digital retail revolution?10:42 - What are we learning about consumers through digital retailing? What does the data reveal?12:15 - New generational trends that are emerging14:12 - How can the auto industry use data to drive recovery? What benefits are we seeing?16:15 - What are cars telling us -- and are they watching their drivers?18:12 - Car data can be a risk assessment19:11 - How this is a time of transition for the auto market and what the future looks like

Auto Industry Navigates Historic Change

David Ferber, SVP of Analytical Capabilities &amp; Solutions at Equifax, and Sri Ambati, Founder and CEO of H2O.ai, discuss the democratization of AI and how companies need to bring together multi-faceted teams to get the most out of their data. Skip ahead to these topics:1:40 - About H2O4:30 - Data is at the heart of all machine learning7:25 - Trying to make decision-making cheaper, faster, easier9:22 - H2O customer stories12:11 - How H2O has recruited brilliant minds from around the world16:45 - Examples of AI for good20:40 - H2O wins award for its good works in India during COVID-19 pandemic22:30 - Looking to our younger generation for inspiration25:00 - The horizon for innovation has shifted since COVID-19

David Ferber, SVP of Analytical Capabilities &amp; Solutions at Equifax, and Sri Ambati, Founder and CEO of <a href="http://H2O.ai" rel="nofollow" target="_blank">H2O.ai</a>, discuss the democratization of AI and how companies need to bring together multi-faceted teams to get the most out of their data.  Skip ahead to these topics: 1:40 - About H2O4:30 - Data is at the heart of all machine learning7:25 - Trying to make decision-making cheaper, faster, easier9:22 - H2O customer stories12:11 - How H2O has recruited brilliant minds from around the world16:45 - Examples of AI for good20:40 - H2O wins award for its good works in India during COVID-19 pandemic22:30 - Looking to our younger generation for inspiration25:00 - The horizon for innovation has shifted since COVID-19

Data Science is a Team Sport

This episode focuses on how companies can collaborate with universities to solve business problems and strengthen their own data science programs. Jeff Dugger, Principal Data Scientist at Equifax &amp; University Research Director, interviews Jennifer Priestley, Professor of Statistics and Data Science at Kennesaw State University, about how university’s structure their data science programs -- and how they rely on the private sector to stay relevant.Skip ahead to these highlights:1:00 - About Kennesaw State University’s School of Data Science and Analytics3:25 - The challenges Jennifer faced when launching one of the first university data science programs6:00 - How to bring different backgrounds together for an effective data science program11:00 - How data science programs differ from so-called “spoke” programs17:05 - It’s all about adding value to the organization18:20 - Understanding model results21:03 - The real-time feedback loop with the private sector26:33 - The need for communications skills - how do you tell the story of your data?28:30 - Top 3 takeaways for companies to build and strengthen their data science programs

This episode focuses on how companies can collaborate with universities to solve business problems and strengthen their own data science programs. Jeff Dugger, Principal Data Scientist at Equifax &amp; University Research Director, interviews Jennifer Priestley, Professor of Statistics and Data Science at Kennesaw State University, about how university’s structure their data science programs -- and how they rely on the private sector to stay relevant. Skip ahead to these highlights: 1:00 - About Kennesaw State University’s School of Data Science and Analytics3:25 - The challenges Jennifer faced when launching one of the first university data science programs6:00 - How to bring different backgrounds together for an effective data science program11:00 - How data science programs differ from so-called “spoke” programs17:05 - It’s all about adding value to the organization18:20 - Understanding model results21:03 - The real-time feedback loop with the private sector26:33 - The need for communications skills - how do you tell the story of your data?28:30 - Top 3 takeaways for companies to build and strengthen their data science programs

How to Strengthen Your Company’s Data Science Program

We interview Mark Miller, director of insights at Comperemedia, about what’s driving the popularity of challenger banks. Also known as neobanks, challenger banks are upstart banks that usually operate solely online, offer innovative features and tend to target specific groups of tech-savvy customers. Join us as we discuss what’s behind this innovative banking trend, how it’s impacting traditional banks and what the future holds for challenger banks.Jump ahead to these highlights:1:15 - About Comperemedia1:35 - Background on challenger banks and what’s driving their success2:23 - How challenger banks are differentiating themselves and the impact on traditional banks3:35 - Who is attracted to challenger banks4:37 - Customer expectations of challenger banks6:48 - Challenger banks find riches in niches6:33 - Challenger banks’ use of alternative data7:44 - Regulatory landscape for challenger banks8:26 - How challenger banks market to customers9:07 - Consumer concerns about challenger banksRESOURCES:For more insights, check out the Comperemedia blog and Mintel blog. For more information about our podcast, visit https://www.equifax.com/business/data-dialogues-podcast/

We interview Mark Miller, director of insights at Comperemedia, about what’s driving the popularity of challenger banks. Also known as neobanks, challenger banks are upstart banks that usually operate solely online, offer innovative features and tend to target specific groups of tech-savvy customers. Join us as we discuss what’s behind this innovative banking trend, how it’s impacting traditional banks and what the future holds for challenger banks. Jump ahead to these highlights: 1:15 - About Comperemedia 1:35 - Background on challenger banks and what’s driving their success 2:23 - How challenger banks are differentiating themselves and the impact on traditional banks 3:35 - Who is attracted to challenger banks 4:37 - Customer expectations of challenger banks 6:48 - Challenger banks find riches in niches 6:33 - Challenger banks’ use of alternative data 7:44 - Regulatory landscape for challenger banks 8:26 - How challenger banks market to customers 9:07 - Consumer concerns about challenger banksRESOURCES: For more insights, check out the <a href="https://welcome.comperemedia.com/blog/" rel="nofollow" target="_blank">Comperemedia blog</a> and <a href="https://www.mintel.com/blog" rel="nofollow" target="_blank">Mintel blog</a>. For more information about our podcast, visit <a href="https://www.equifax.com/business/data-dialogues-podcast/" rel="nofollow" target="_blank">https://www.equifax.com/business/data-dialogues-podcast/</a>

Comperemedia: The Lure of Challenger Banks

In this episode of Data Dialogues, we interview Hany Fam, founder and CEO of Markaaz, about the top three things small businesses should do right now -- and which digital solutions can help them be successful.Jump ahead to these highlights:0:54 - About Hany Fam, founder of Markaaz2:17 - What is the Markaaz marketplace?3:55 - What are point-to-point solutions?4:45 - How data savvy are small businesses?8:33 - The small business data experience9:01 - How small businesses leverage data to power their business11:07 - How to take a data-driven approach13:30 - Growth of e-commerce15:33 - Benefits of digitization for small businesses18:10 - Educating on small business20:27 - Cash flow22:47 - 3 things business owners should do in 202123:47  - Buy now, pay later

In this episode of Data Dialogues, we interview Hany Fam, founder and CEO of Markaaz, about the top three things small businesses should do right now -- and which digital solutions can help them be successful. Jump ahead to these highlights: 0:54 - About Hany Fam, founder of Markaaz2:17 - What is the Markaaz marketplace?3:55 - What are point-to-point solutions?4:45 - How data savvy are small businesses?8:33 - The small business data experience9:01 - How small businesses leverage data to power their business11:07 - How to take a data-driven approach13:30 - Growth of e-commerce15:33 - Benefits of digitization for small businesses18:10 - Educating on small business20:27 - Cash flow22:47 - 3 things business owners should do in 202123:47  - Buy now, pay later

Markaaz: Demystifying Data for Small Business

A podcast where innovative business leaders discuss data: how to think about, how to use it and how it can help us all make better business decisions every day. As they tell their stories of trials and triumphs, you’ll gain key insights to leverage in your own day-to-day operations.

In this episode of Data Dialogues, Equifax Marketing VP, Elizabeth Fairman, interviews Equifax Chief Privacy Officer, Nick Oldham, about how society is transitioning from the explosion of data collection to the responsible stewardship of that data. Oldham explains that we have to move away from transactional compliance requirements and focus more on doing the right thing. Jump ahead to these highlights::47 - Oldham’s professional background2:40 - How data privacy has evolved over time6:15 - Data privacy regulation trends around the globe9:05 - What data ethics means as a consumer and as a business12:30 - How businesses should approach data privacy15:30 - Implications of data tracking software18:25 - What are the security issues around data usage: access vs. usage21:10 - Moving to a model of “doing the right thing” and education around data usage22:40 - Equifax focused on data privacy ethics

In this episode of Data Dialogues, Equifax Marketing VP, Elizabeth Fairman, interviews Equifax Chief Privacy Officer, Nick Oldham, about how society is transitioning from the explosion of data collection to the responsible stewardship of that data. Oldham explains that we have to move away from transactional compliance requirements and focus more on doing the right thing.  Jump ahead to these highlights: :47 - Oldham’s professional background2:40 - How data privacy has evolved over time6:15 - Data privacy regulation trends around the globe9:05 - What data ethics means as a consumer and as a business12:30 - How businesses should approach data privacy15:30 - Implications of data tracking software18:25 - What are the security issues around data usage: access vs. usage21:10 - Moving to a model of “doing the right thing” and education around data usage22:40 - Equifax focused on data privacy ethics

Data Privacy: Doing the Right Thing

In this episode of Data Dialogues, we interview Carol Kruse, a Valvoline board member and former marketing executive at Cambia Health Solutions, ESPN and Coca-Cola. She weighs in on which data to focus on -- and how to sell your findings to others in your organization.Jump ahead to these highlights:1:00 - Carol Kruse’s career path4:30 - How Carol turns to data to tell her stories7:30 - Is it better to dig deeper into the data or let the data tell the story?11:25 - How do you best tell someone to trust the data?12:20 - The marriage of art and science14:38 - Overcoming objections to data17:30 - Which data to focus on21:30 - The end user24:55 - Focus groups25:50 - Example of combining creative with actionable data

In this episode of Data Dialogues, we interview Carol Kruse, a Valvoline board member and former marketing executive at Cambia Health Solutions, ESPN and Coca-Cola. She weighs in on which data to focus on -- and how to sell your findings to others in your organization. Jump ahead to these highlights: 1:00 - Carol Kruse’s career path 4:30 - How Carol turns to data to tell her stories 7:30 - Is it better to dig deeper into the data or let the data tell the story? 11:25 - How do you best tell someone to trust the data? 12:20 - The marriage of art and science 14:38 - Overcoming objections to data 17:30 - Which data to focus on 21:30 - The end user 24:55 - Focus groups 25:50 - Example of combining creative with actionable data

Storytelling Through Data

In this episode of Data Dialogues, we explain how smart data can help organizations combat the growing digital threat of identity fraud. Aparna Sheth, product leader for Equifax’s Identity and Fraud Solutions Group, interviews Cori Shen, who leads a data science team responsible for data and machine learning and AI-driven product innovations to solve identity and fraud challenges. Jump ahead to these highlights:0:40 - Cori’s role and team responsibilities0:54 - Consumers shift from digital-first to digital-only business environment1:37 - Fraud has multiplied2:18 - New fraud opportunities emerge during unprecedented economic conditions3:36 - How to use data and analytics to solve fraud4:41 - How smart data works8:40 - Role of digital signals and bureau data10:00 - Explaining graph networks10:58 - How to make the insights actionable and examples14:38 - Our smart data approachPodcast TranscriptionAparna:Welcome to Data Dialogues.  Today, we are discussing how smart data can help organizations fight the evolving challenges of identity fraud. My name is Aparna Sheth. I'm a product leader here at Equifax in our identity and fraud solutions group. And I'm so happy to have Cori Shen here with me, who leads our data science team. Hi, Cori, would you like to share more about what you do?Cori:Sure. Thanks, Aparna. Happy to be here too. And I'm glad that we can discuss this topic together. I'm Cori Shen. I lead our identity and fraud data science team for Equifax.Aparna:Alright. So speaking of identity and fraud, 2020 has been quite a year. COVID accelerated digital transformation across the board. We saw a stark paradigm shift take place last year, where we went from a “digital first” to “digital only” business environment. And this was of course brought on by abrupt shelter in place orders.Cori:That's right, Aparna. I totally agree with you. You know, consumers were forced to do everything online from buying groceries to ordering food. And of course they're doing all their financial transactions online. You know, last year 80% of my groceries were done through a mobile app.Aparna:Oh wow.  Yeah, I know. And we saw during this pandemic that not only did the new fraud schemes emerge, but we also saw the existing types of fraud have multiplied. Right?Cori:That's absolutely true. You probably saw this report coming from the Federal Trade Commission, right?  The report shows they have received about, I think 275,000 fraud complaints last year. And also when we track the fraud trends in our own data, we see that the authorized user abuse risk in 2020 went up by over 23% compared to 2019 and 2018.Aparna:Wow. The other factor, of course, was the unprecedented unemployment rates and economic downturn. And to combat that, as we all know, Congress passed trillion plus dollars of stimulus relief packages to help struggling families and boost the economy. We saw new fraud schemes in March exploiting PPP, which is the Payroll Protection Program, as well as the expanded unemployment insurance program.So as millions of Americans were applying for help, we had these international and national criminal rings that were working relentlessly to steal these funds, using sophisticated methods of identity theft.Cori:That's right, Aparna. You know, with all the relief money that went to the market in 2020, I think it really made fraudsters go all out on it. As a matter of fact, these fraud schemes might be new, but the underlying fraud challenges are the same ones like synthetic ID, the compromised ID, which has been around for years. And I think that's why now more than ever, we need something better in identity and fraud prevention.Aparna:I couldn't agree more. So let's talk about how we can use data and analytics to solve this, right? There is just so much data out there. Not just related to our credit file, but also every digital interaction that we make as individuals. Be it social media or when we shop online. So how do we sort through these billions of interactions and use analytics to really drive those insights that can be used to mitigate against these growing challenges?Cori:This is a great question.  Because if we look at today's digital paradigm, managing big data from multiple sources is no longer a challenge. What matters most is how to make sense of big data and how to intelligently and efficiently assemble multi-source data for the right insights. And we will call it smart data because we want data to talk, and we want data to be able to offer recommendations.Aparna:I love it. Smart data. I mean, it sounds fantastic, right? But it's easier said than done, isn't it? Let's take synthetic identities for example.  We know that many of these have been in the system for a while and they look like legitimate people. Very often their identity information is complete, and it matches to what systems have. As a matter of fact, sometimes they even have a matched social media profile. That's why these fake identities look like real people and can be used to create fake businesses, defraud the system with millions of dollars of PPP or employment claims. Right? So even if we do identity verification matches from multiple sources, we may not be able to catch them. So what should we do?Cori:Ah, what should we do? This is exactly the right question. I totally agree with you. If we're just talking about matching identities from multiple sources, it is not smart data. Smart data has two components: insights and connections. We think a real effective way to build smart data is to connect to the useful insights from a graph network perspective. Let me take synthetic ID detection for example. Here is how you can build.  First, build useful insights from multiple sources. You want to search for the abnormal signals throughout an identity's lifecycle. To do so you will need the consumer activity data from multiple sources and from multiple systems. For example, the consumer applies for credit cards or loans. The consumer checks their credit online. They enroll. We're logging into an online system. They're making payments. They're making purchases from e-commerce sites. All these different data points are consumer activity data.We all know that we cannot listen to what fraudsters say. But we need to watch what they do. Because fraudsters will give you a fake ID and tell you, Hey, everything's good. Everything matched. And I want to borrow $50,000. But when you get the power of the consumer activity data, what you can do is that you can look closely into their activities. And then, you will find out a lot of secrets about them. And here are some examples. All the synthetic ID outliers appear at an early stage. You will see some synthetic IDs apply for mortgages and shop for luxury cars. However, when you look at the activity pattern for a regular legit consumer at the earlier stage, you will often see they only apply for cell phone, apartments, internet service, credit cards.  These types of starter programs. Another example, sometimes synthetic ID can be a very patient game. This means that, you know, fraudsters can wait for a couple years to build their credit history before they take action. However, the interesting thing about their activity is that once they start taking actions, they do it super fast to an extreme extent. So this means that when you explore the trended activities, you see these ideas can be dormant for a while. And then all of sudden you see a huge spike in their activities. These huge spikes are usually something like they are desperately shopping around for money all over the place. For example, they try to get as many credit cards or loans as possible from lots of different institutions. Also, they will act extremely anxiously in monitoring their credit. It's like they're doing this every day while they're shopping for money.Aparna:This is very interesting. Thanks for sharing these secrets on consumer behavior anomalies, and how they can be used in synthetic identity detection. So what about the digital signals and bureau data? I would think they are also very useful in identifying synthetic identities, right?Cori:Digital signals are definitely powerful and critical. Here's another example about synthetic ID to establish and maintain synthetic ID. The fraudsters like to manipulate identities via online channels. They like to change addresses and alter names online or from their mobile phones. At that time, you may see there could be the same device links to many different IDs for name and address change request. You may also see that the IP geo location is far away from the existing addresses they're using and the new addresses they requested. Speaking of bureau data, it is also really helpful when you use them to explore the risk of signals like piggybacking credit using authorized user abuse schemes.Aparna:Ah, I see. So it's, it's really neat to see how we can derive so many different insights to look for anomalies and then use those for synthetic identity detection. So Cori, you earlier, you mentioned that one recommended way to assemble for smart data is to connect these insights in a graph network. I'm aware of link analysis, which is a very effective tool used in fraud review. Can you tell me a little bit more about graph networks? Is it the same tool you use in your lab?Cori:It's a little bit different from visualization. So what I'm saying is that what we do in our innovation lab is not to run one or two graphs. In order to find the true meaning of the connections, we need to build graph networks on very large scale data, like billions of transactions.Aparna:Wow. Building graphs on billions of records. I mean, it's impressive, but there is a lot of information. So how could you make sense of these connections so that the outcome from it can be actionable? You know, versus something that's too abstract and which cannot be easily explained?Cori:This is a really good point because it is very important for our smart data to not only be predictive, but also prescriptive. So because of that, let me explain to you how you can make sense of the connections when we are processing billions of transactions. And then you can come up with the actionable recommendations through our work. So basically this is a machine learning capability. You can build with a graph database on a scalable and distributed system such as Google cloud. So this is what you're gonna do. So first you can link all the identities from billions of transactions based on address, phone number, email and device. So what I mean by linking is for example, a group of family members can be linked and connected to each other, as they might be living in the same place, using the same address. However, two strangers probably cannot be connected directly because there's no reason they will live together or they will use the same email accounts. So by doing this linking, you are connecting the identities. So now you're going to have millions of groups, right? Some groups connect more people and some groups connect less people. So next, you can then assign these synthetic ID insights… the ones we just talked about earlier, remember? The authorized user abuse risk, consumer activity pattern outliers, or the high risk digital signals. You can assign them to the identities in each group. So this way, by connecting the identities, now you're indeed connecting the insights.Aparna:Let me see if I've got this. So the first thing this tool does is link people and tie them together in a group based on some PII, right? And then you layer in the synthetic identity insights that we discussed earlier to detect anomalies, which will possibly indicate synthetic identities? Is that correct?Cori:That is absolutely right. So to put into a more concrete perspective. For example, when you have this connection, and you may find a group with a hundred people in it. And in this group, you can see some strangers are connected to each other, and you can also see some people in this group showing one or multiple synthetic ID risks. Those synthetic ID insights. And now what does that mean to you is that this group is indeed a synthetic ID crime ring.Aparna:Now I can see the whole picture of how we build smart data for this use case. So you derive insights, then you connect those insights to discover a synthetic identity fraud ring. And once we do that, we can take actions, right? We can conduct fraud reviews, we can do step up authentication against these synthetic IDs and stop them from stealing money, right?Cori:That's absolutely right. So this is our smart data approach. We assembled data insights differently for a very effective fraud detection. And moreover the outcome from the connected insights like you just mentioned is indeed actionable.Aparna:Thanks Cori. This really helps me, and I'm sure our listeners as well understand how our smart data strategies can be used to mitigate identity and fraud threats. So to summarize, the amount of digital transactions and data is growing exponentially since the pandemic began last year. So it is really critical we can use this data and assemble it in a way to make the data talk as in smart data to derive actionable insights. And we have seen organizations who have recognized this and have been data-driven and proactive to be very successful in combating identity and fraud challenges for this post pandemic era. Thank you so much, Cori, for discussing this topic with me today. It was a lot of fun.

In this episode of Data Dialogues, we explain how smart data can help organizations combat the growing digital threat of identity fraud. Aparna Sheth, product leader for Equifax’s Identity and Fraud Solutions Group, interviews Cori Shen, who leads a data science team responsible for data and machine learning and AI-driven product innovations to solve identity and fraud challenges.  Jump ahead to these highlights: 0:40 - Cori’s role and team responsibilities 0:54 - Consumers shift from digital-first to digital-only business environment 1:37 - Fraud has multiplied 2:18 - New fraud opportunities emerge during unprecedented economic conditions 3:36 - How to use data and analytics to solve fraud 4:41 - How smart data works 8:40 - Role of digital signals and bureau data 10:00 - Explaining graph networks 10:58 - How to make the insights actionable and examples 14:38 - Our smart data approach Podcast Transcription Aparna:Welcome to Data Dialogues.  Today, we are discussing how smart data can help organizations fight the evolving challenges of identity fraud. My name is Aparna Sheth. I'm a product leader here at Equifax in our identity and fraud solutions group. And I'm so happy to have Cori Shen here with me, who leads our data science team. Hi, Cori, would you like to share more about what you do? Cori:Sure. Thanks, Aparna. Happy to be here too. And I'm glad that we can discuss this topic together. I'm Cori Shen. I lead our identity and fraud data science team for Equifax. Aparna:Alright. So speaking of identity and fraud, 2020 has been quite a year. COVID accelerated digital transformation across the board. We saw a stark paradigm shift take place last year, where we went from a “digital first” to “digital only” business environment. And this was of course brought on by abrupt shelter in place orders. Cori:That's right, Aparna. I totally agree with you. You know, consumers were forced to do everything online from buying groceries to ordering food. And of course they're doing all their financial transactions online. You know, last year 80% of my groceries were done through a mobile app. Aparna:Oh wow.  Yeah, I know. And we saw during this pandemic that not only did the new fraud schemes emerge, but we also saw the existing types of fraud have multiplied. Right? Cori:That's absolutely true. You probably saw this report coming from the Federal Trade Commission, right?  The report shows they have received about, I think 275,000 fraud complaints last year. And also when we track the fraud trends in our own data, we see that the authorized user abuse risk in 2020 went up by over 23% compared to 2019 and 2018. Aparna:Wow. The other factor, of course, was the unprecedented unemployment rates and economic downturn. And to combat that, as we all know, Congress passed trillion plus dollars of stimulus relief packages to help struggling families and boost the economy. We saw new fraud schemes in March exploiting PPP, which is the Payroll Protection Program, as well as the expanded unemployment insurance <a href="http://program.So" rel="nofollow" target="_blank">program.So</a> as millions of Americans were applying for help, we had these international and national criminal rings that were working relentlessly to steal these funds, using sophisticated methods of identity theft. Cori:That's right, Aparna. You know, with all the relief money that went to the market in 2020, I think it really made fraudsters go all out on it. As a matter of fact, these fraud schemes might be new, but the underlying fraud challenges are the same ones like synthetic ID, the compromised ID, which has been around for years. And I think that's why now more than ever, we need something better in identity and fraud prevention. Aparna:I couldn't agree more. So let's talk about how we can use data and analytics to solve this, right? There is just so much data out there. Not just related to our credit file, but also every digital interaction that we make as individuals. Be it social media or when we shop online. So how do we sort through these billions of interactions and use analytics to really drive those insights that can be used to mitigate against these growing challenges? Cori:This is a great question.  Because if we look at today's digital paradigm, managing big data from multiple sources is no longer a challenge. What matters most is how to make sense of big data and how to intelligently and efficiently assemble multi-source data for the right insights. And we will call it smart data because we want data to talk, and we want data to be able to offer recommendations. Aparna:I love it. Smart data. I mean, it sounds fantastic, right? But it's easier said than done, isn't it? Let's take synthetic identities for example.  We know that many of these have been in the system for a while and they look like legitimate people. Very often their identity information is complete, and it matches to what systems have. As a matter of fact, sometimes they even have a matched social media profile. That's why these fake identities look like real people and can be used to create fake businesses, defraud the system with millions of dollars of PPP or employment claims. Right? So even if we do identity verification matches from multiple sources, we may not be able to catch them. So what should we do? Cori:Ah, what should we do? This is exactly the right question. I totally agree with you. If we're just talking about matching identities from multiple sources, it is not smart data. Smart data has two components: insights and connections. We think a real effective way to build smart data is to connect to the useful insights from a graph network perspective. Let me take synthetic ID detection for example. Here is how you can build.  First, build useful insights from multiple sources. You want to search for the abnormal signals throughout an identity's lifecycle. To do so you will need the consumer activity data from multiple sources and from multiple systems. For example, the consumer applies for credit cards or loans. The consumer checks their credit online. They enroll. We're logging into an online system. They're making payments. They're making purchases from e-commerce sites. All these different data points are consumer activity data. We all know that we cannot listen to what fraudsters say. But we need to watch what they do. Because fraudsters will give you a fake ID and tell you, Hey, everything's good. Everything matched. And I want to borrow $50,000. But when you get the power of the consumer activity data, what you can do is that you can look closely into their activities. And then, you will find out a lot of secrets about them. And here are some examples. All the synthetic ID outliers appear at an early stage. You will see some synthetic IDs apply for mortgages and shop for luxury cars. However, when you look at the activity pattern for a regular legit consumer at the earlier stage, you will often see they only apply for cell phone, apartments, internet service, credit cards.  These types of starter programs. Another example, sometimes synthetic ID can be a very patient game. This means that, you know, fraudsters can wait for a couple years to build their credit history before they take action. However, the interesting thing about their activity is that once they start taking actions, they do it super fast to an extreme extent. So this means that when you explore the trended activities, you see these ideas can be dormant for a while. And then all of sudden you see a huge spike in their activities. These huge spikes are usually something like they are desperately shopping around for money all over the place. For example, they try to get as many credit cards or loans as possible from lots of different institutions. Also, they will act extremely anxiously in monitoring their credit. It's like they're doing this every day while they're shopping for money. Aparna:This is very interesting. Thanks for sharing these secrets on consumer behavior anomalies, and how they can be used in synthetic identity detection. So what about the digital signals and bureau data? I would think they are also very useful in identifying synthetic identities, right? Cori:Digital signals are definitely powerful and critical. Here's another example about synthetic ID to establish and maintain synthetic ID. The fraudsters like to manipulate identities via online channels. They like to change addresses and alter names online or from their mobile phones. At that time, you may see there could be the same device links to many different IDs for name and address change request. You may also see that the IP geo location is far away from the existing addresses they're using and the new addresses they requested. Speaking of bureau data, it is also really helpful when you use them to explore the risk of signals like piggybacking credit using authorized user abuse schemes. Aparna:Ah, I see. So it's, it's really neat to see how we can derive so many different insights to look for anomalies and then use those for synthetic identity detection. So Cori, you earlier, you mentioned that one recommended way to assemble for smart data is to connect these insights in a graph network. I'm aware of link analysis, which is a very effective tool used in fraud review. Can you tell me a little bit more about graph networks? Is it the same tool you use in your lab? Cori:It's a little bit different from visualization. So what I'm saying is that what we do in our innovation lab is not to run one or two graphs. In order to find the true meaning of the connections, we need to build graph networks on very large scale data, like billions of transactions. Aparna:Wow. Building graphs on billions of records. I mean, it's impressive, but there is a lot of information. So how could you make sense of these connections so that the outcome from it can be actionable? You know, versus something that's too abstract and which cannot be easily explained? Cori:This is a really good point because it is very important for our smart data to not only be predictive, but also prescriptive. So because of that, let me explain to you how you can make sense of the connections when we are processing billions of transactions. And then you can come up with the actionable recommendations through our work. So basically this is a machine learning capability. You can build with a graph database on a scalable and distributed system such as Google cloud. So this is what you're gonna do. So first you can link all the identities from billions of transactions based on address, phone number, email and device. So what I mean by linking is for example, a group of family members can be linked and connected to each other, as they might be living in the same place, using the same address. However, two strangers probably cannot be connected directly because there's no reason they will live together or they will use the same email accounts. So by doing this linking, you are connecting the identities. So now you're going to have millions of groups, right? Some groups connect more people and some groups connect less people. So next, you can then assign these synthetic ID insights… the ones we just talked about earlier, remember? The authorized user abuse risk, consumer activity pattern outliers, or the high risk digital signals. You can assign them to the identities in each group. So this way, by connecting the identities, now you're indeed connecting the insights. Aparna:Let me see if I've got this. So the first thing this tool does is link people and tie them together in a group based on some PII, right? And then you layer in the synthetic identity insights that we discussed earlier to detect anomalies, which will possibly indicate synthetic identities? Is that correct? Cori:That is absolutely right. So to put into a more concrete perspective. For example, when you have this connection, and you may find a group with a hundred people in it. And in this group, you can see some strangers are connected to each other, and you can also see some people in this group showing one or multiple synthetic ID risks. Those synthetic ID insights. And now what does that mean to you is that this group is indeed a synthetic ID crime ring. Aparna:Now I can see the whole picture of how we build smart data for this use case. So you derive insights, then you connect those insights to discover a synthetic identity fraud ring. And once we do that, we can take actions, right? We can conduct fraud reviews, we can do step up authentication against these synthetic IDs and stop them from stealing money, right? Cori:That's absolutely right. So this is our smart data approach. We assembled data insights differently for a very effective fraud detection. And moreover the outcome from the connected insights like you just mentioned is indeed actionable. Aparna:Thanks Cori. This really helps me, and I'm sure our listeners as well understand how our smart data strategies can be used to mitigate identity and fraud threats. So to summarize, the amount of digital transactions and data is growing exponentially since the pandemic began last year. So it is really critical we can use this data and assemble it in a way to make the data talk as in smart data to derive actionable insights. And we have seen organizations who have recognized this and have been data-driven and proactive to be very successful in combating identity and fraud challenges for this post pandemic era. Thank you so much, Cori, for discussing this topic with me today. It was a lot of fun.

Using Smart Data to Combat Identity Fraud

In this episode of Data Dialogues, Equifax Marketing VP Tricia Gabberty, and Alice Siu, Associate Director at the Center for Deliberative Democracy at Stanford University, discuss the role that brands and consumers must play when trying to ensure quality, accurate data.Jump ahead to these highlights:0:50 - Alice’s role at the Center for Deliberative Democracy2:15 - The definition of data quality7:15 - Supplementing raw data with outside data10:41 - The privacy conundrum13:51 - What to know when seeking a data provider16:12 - Data sources or techniques to avoid19:30 - What should consumers consider when reading polls and surveys23:46 - A warning about news recommender engines26:28 - Gathering reliable Gen Z research

In this episode of Data Dialogues, Equifax Marketing VP Tricia Gabberty, and Alice Siu, Associate Director at the Center for Deliberative Democracy at Stanford University, discuss the role that brands and consumers must play when trying to ensure quality, accurate data. Jump ahead to these highlights: 0:50 - Alice’s role at the Center for Deliberative Democracy2:15 - The definition of data quality7:15 - Supplementing raw data with outside data10:41 - The privacy conundrum13:51 - What to know when seeking a data provider16:12 - Data sources or techniques to avoid19:30 - What should consumers consider when reading polls and surveys23:46 - A warning about news recommender engines26:28 - Gathering reliable Gen Z research

Stanford University: Is Your Data Good Quality?

In the second episode of Data Dialogues, we interview Aaron J. Webster, Chief Risk Officer at SoFi. He discusses how data is helping its members achieve financial success.Jump ahead to these highlights: 0:55 - About Aaron J. Webster's role at SoFi 2:57 - Using data to get the right products into the right hands 8:10 - Cultivating trust with customers online 12:07 - How to continue the customer lifecycle and maintain relationships 15:15 - Overcoming the privacy "creep" factor 16:40 - It's about the "efficient frontier" 18:54 - Growth and building a sustainable, resilient business portfolio 21:12 - Phenomenal business continuity 23:11 - How did you prepare for economic turmoil? 24:48 - How important is data efficacy and accuracy? 27:03 - Balancing customer service and privacy 29:00 - What's next? 31:00 - Best piece of advice: Treat data as electricity 

In the second episode of Data Dialogues, we interview Aaron J. Webster, Chief Risk Officer at SoFi. He discusses how data is helping its members achieve financial success. Jump ahead to these highlights:<ul> <li>0:55 - About Aaron J. Webster's role at SoFi</li> <li>2:57 - Using data to get the right products into the right hands</li> <li>8:10 - Cultivating trust with customers online</li> <li>12:07 - How to continue the customer lifecycle and maintain relationships</li> <li>15:15 - Overcoming the privacy "creep" factor</li> <li>16:40 - It's about the "efficient frontier"</li> <li>18:54 - Growth and building a sustainable, resilient business portfolio</li> <li>21:12 - Phenomenal business continuity</li> <li>23:11 - How did you prepare for economic turmoil?</li> <li>24:48 - How important is data efficacy and accuracy?</li> <li>27:03 - Balancing customer service and privacy</li> <li>29:00 - What's next?</li> <li>31:00 - Best piece of advice: Treat data as electricity</li> </ul>

SoFi: Treat Data as Electricity

Anthony Mavromatis, VP of Customer Marketing Analytics and Data Science for American Express, talks about how his organization has used personalization to deliver a world-class customer experience from an omni-channel perspective -- and what that journey looked like.Jump ahead to these highlights: 0:48 - Anthony's role at American Express 1:20 - How do you define personalization? 2:46 - The personalization journey for American Express 5:53 - Orchestra, the centralized solution for delivering on personalization vision 10:20 - Importance of bringing data together for customer experience 12:58 - Creating a marketer's Candyland 15:18 - Balancing customer expectations with privacy 17:22 - Advice for embracing on a personalization journey 20:50 - How much to rely on consumer-in, voice-of-customer data If you like our episode, please subscribe so you're notified of future episodes.

Anthony Mavromatis, VP of Customer Marketing Analytics and Data Science for American Express, talks about how his organization has used personalization to deliver a world-class customer experience from an omni-channel perspective -- and what that journey looked like. Jump ahead to these highlights:<ul> <li>0:48 - Anthony's role at American Express</li> <li>1:20 - How do you define personalization?</li> <li>2:46 - The personalization journey for American Express</li> <li>5:53 - Orchestra, the centralized solution for delivering on personalization vision</li> <li>10:20 - Importance of bringing data together for customer experience</li> <li>12:58 - Creating a marketer's Candyland</li> <li>15:18 - Balancing customer expectations with privacy</li> <li>17:22 - Advice for embracing on a personalization journey</li> <li>20:50 - How much to rely on consumer-in, voice-of-customer data</li> </ul>If you like our episode, please subscribe so you're notified of future episodes.

American Express: The Personalization Journey

Data Dialogues

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