A day in the life of the gentleman banker was once described by the 3-6-3 rule – accept deposits at three percent, loan money at six percent and tee off at the golf course at 3 p.m. The financial services industry can rightfully state that it has come a long way since then. It has implemented technological innovation and managed risk in a constantly changing economic environment over several decades. The gentleman banker has since evolved into a sophisticated financial risk manager who works within a complex framework of rules and regulations with tens of trillions of dollars of assets under management.
Fintechs have moved at a much faster pace than banks in some areas. They have disrupted the financial services industry with user-centric solutions enabled by technology. These solutions emulate products and services offered by financial institutions. However, these remarkable examples of innovation have largely ignored the elephant in the room – regulation. Read more
“Facebook today cannot exist without AI. Every time you use Facebook or Instagram or Messenger, you may not realize it, but your experiences are being powered by AI.” — Joaquin Candela, Facebook’s Applied ML team leader
“Machine learning is a core transformative way by which we are rethinking everything we are doing. Google Brain is the way we are embedding this in everything we do.” Sundar Pichai (CEO Google)
Salesforce CEO Marc Benioff said at a recent conference: “This [AI, ML] is a huge shift going forward, which is that everybody wants systems that are smarter, everybody wants systems that are more predictive, everybody wants everything scored, everybody wants to understand what’s the next best offer, next best opportunity, how to make things a little bit more efficient.”
Machine Learning (ML), Deep Learning and AI powering “Systems that Learn at scale” are at the bleeding edge of data science, deep learning and predictive search today.
Every market leader is jumping on this AI enabled engagement trend in retail, banking and healthcare.
Machine learning is central to Facebook’s future. Creating the extreme personalized experience (“individual equation based on 1000s of attributes – your preferences, predilections, conversations and transactions “) is the killer app.
FaceBook – AI/ML Case Study
Facebook is a bleeding edge case study of where AI/ML are being used to augment user engagement and experiences. I am starting to see many leading firms investing in ML Accelerators and Platforms as part of their data science strategy.
New data driven FinTech business models built on Hadoop, Spark and Machine Learning are rapidly emerging and disrupting wealth management. Here is my recent posting from disruptivedigital.wordpress.com about one such use case… Robo-Advisors.
We are in the early stages of a generational shift in wealth management, especially “plain vanilla” investing for the mass affluent and millennial segment. Until recently, you had only two options when investing:
- Do-it-yourself (DIY)
- Hire a registered investment advisor (RIA)
Now there is a third option. Robo-advisors are new class of personal financial advisors that provides online, algorithm based portfolio management with minimal human intervention. Robo-Advisors going after the low-end of brokerage/RIA business with automated asset allocation using Modern Portfolio Theory.
The Robo-Advisors market leaders who are serving the mass affluent include are:
- Wealthfront (with over USD 2.6bn in assets under management (AuM) and 20,000 investors);
- Betterment (with over USD 1.4bn in AuM and 70,000 investors); and
- FutureAdvisor (With over $600 million in AUM).
The timing for this market shift coincides with three trends: consumerization, digital tools, and disillusionment with status-quo investment advisors. The gyrating stock market driven by program trading is increasingly bringing Robo-Advisors, algorithmic portfolio management to the forefront. Investors are getting disillusioned with traditional investment advisors who simply track the market indices (SPY, QQQ or Russell 2000) by purchasing ETFs at best.
Many banks and brokerage firms over the years have shifted their focus to serve ultra high net worth (UHNW) and high net worth (HNW) investors, leaving an opportunity for firms to target the “mass affluent” investors, or those with less than $1 million in investable assets. Younger investors are increasingly interested in online digital advice (trial-and-error bets), as opposed to hiring an adviser.
- Where do customers abandon the shopping process? Is it the same in every geography?
- Audience of One…. Who are your fans versus haters in the marketplace?
- How do customers feel about your products? How engaged are customers with your brand versus your competitors’ brands across social media and web channels?
Fortune 500 companies are making large investments around Programmatic Marketing, Sales and Service (“marketing that learns”). Programmatic marketing is the application of automated technology through which media buyers and sellers may align organizational processes in support of ongoing, channel-agnostic customer engagement (and to allow for the continuous optimization of that effort as business strategies evolve) in order to drive revenue.
One of most often implemented use case in Programmatic Marketing is customer journey mapping and analytics.
Why? Because, deciphering the nuts-and-bolts” of individual customer journeys online (and deducing intent) is core to improving customer experience and driving brand loyalty.
Specifically, the objectives are:
- Visualize and map the end-to-end customer journey by personas
- Optimizing on the right journey attributes to increase yields by >30% lift… Uncover the right combination of web, mobile and physical channels, content and experiences that best achieves the target goals
- Enable marketers to identify journey bottlenecks for individuals and aggregates
- Leverage actual behavior data to enhance and personalize the experience for each individual customer
More data + Better models + More accurate metrics + Better approaches & architectures = Lots of room for improvement!
There are clearly massive foundational shifts taking place around big data. I am not sure how large conventional Fortune 500 firms can innovate and keep up with what’s going on. I have run into CIOs who have not heard of Hadoop in some cases.
It’s also fascinating to see how data-driven “bleeding” edge firms like NetFlix are pushing the envelope. Netflix stats are amazing: 1/3+ Internet traffic (NA / peak); 100+ Million hours per day; 65+ Million members / 50+ countries; 500 Billion Events / Day.
NetFlix is clearly reinventing Television and targeting 90 million potential subs in the US market alone. Binge-watching, cord-cutting are now part of our everyday lingo. What most people don’t realize is how data-driven Netflix is…. from “giving viewers what they want” to “leveraging data mining to boost subscriber base”.
Viewing -> Improved Personalization -> Better Experience is the virtuous circle.
Here is a glimpse at how their BI landscape has evolved in the past five years as they integrate 5 million to 6 million net adds for several years now. The figures are from a presentation by Blake Irvine, Manager Data Science and Engineering.
BI tools @ NetFlix pre-Hadoop
New Technologies | New Possibilities
As a C-level executive, it’s becoming clear to me that NoSQL databases and Machine Learning toolsets like Spark are going to play an increasingly big role in data-driven business models, low-latency architecture & rapid application development (projects that can be done in 8-12 weeks not years).
The best practice firms are making this technology shift as decreasing storage costs have led to an explosion of big data. Commodity cluster software, like Hadoop, has made it 10-20x cheaper to store large datasets.
After spending two days at the leading NoSQL provider MongoDB World event in NYC, I was pleasantly surprised to see the amount of innovation and size of user community around document centric databases like MongoDB.
Data Driven Insight Economy
It doesn’t take genius to realize that data driven business models, high volume data feeds, mobile first customer engagement, and cloud are creating new distributed database requirements. Today’s modern online and mobile applications need continuous availability, cost effective scalability and high-speed analytics to deliver an engaging customer experience.
We know instinctively that there is value in all the data being captured in the world around out…no question is no longer “if there is value” but “how to extract that value and apply it to the business to make a difference”.
Legacy relational databases fail to meet the requirements of digital and online applications for the following reasons:
“Have we got a girl for you” Some very sophisticated machine learning and predictive analytics models are powering the online dating or hookup world.
A lot of innovation is taking place around real-time, geo-location based matching services. Coinciding with the trend toward mobile, there is a meaningful shift of usage from desktop to mobile devices. The mobile trend also enables tailored dating products to meet the varying romantic and hookup preferences of users.
Take for Match.com which debuted its online dating first site in the U.S. in April 1995. Today, the Match.com brand hosts sites in 24 countries, in fifteen different languages spanning five continents. Match.com offers an interactive way for singles to meet other singles with whom they might otherwise never cross paths.
How to model and predict human attraction? Match.com is powered by Synapse algorithm. Synapse learns about its users in ways similar to sites like Amazon, Neflix, and Pandora to recommend new products, movies, or songs based on a user’s preferences.
Enabling dating in a digital world… Match.com uses Chemistry.com to do personalized surveys and get detailed preference data. But when it comes to matching people based on their potential love and mutual attraction, however, analytics get significantly more complex when you are attempting to predict mutual match… the person A is a potential match for person B…. but with high probability that person B is also interested in person A. Read more