Fintech in 2017: Risk management takes center stage

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.

Applied Machine Learning and AI @ FaceBook

“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

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“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)

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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.

Chief Data Officer Role & Challenges

Big Data has been replaced by Machine Learning and AI as the next “must have” trend. Machine learning has caught the attention of venture capitalists.

Chief Data Officers, Chief Analytics Officers,  Chief Data Science Officers and Chief Digital Officers are everywhere. The job is to leverage the latest in predictive analytics, data science, machine learning, and multi-tenant cloud architecture to bring innovation to traditional processes.

This is a pivotal moment in data driven business models (“systems that learn”) but there is no getting around the inherent difficulties associated with either altering organizational behavior, data ownership politics or managing transformation of the data infrastructure. And while the challenges are real, many firms are getting closer to achieving a data science and data management environment.

What are data and analytics officers overseeing… A variety of foundational & plumbing strategies:

• Data-as-a-Service:  Data Provisioning, Management, Lineage, Quality
• Reporting-as-a-Service:  Dashboards, KPIs, Drilldowns/Aggregates…. Descriptive
• Analytics-as-a-Service:  Predictive  Modeling and BI… Prescriptive analytics
• Information-as-a-Service:  Threshold based Alerts, Exceptions, Mobile Prompts
• Insights-as-a-Service:   ML/AI based “Systems that Learn”…automated learning – augmented intelligence, Next best Offer/Action

At core of all these, Data Management and Data Science tools are core technical and business capabilities.  Some firms are more mature and further along than others.

Why Mature Data Management as a Function

Organizations live or die by the quality of their data.  

Data is an underlying factor of input into business operations and essential in order to  facilitate process automation, digitize operations, support financial engineering and enhance customer facing analytical capabilities.

An effective data management program requires a planned strategic effort

• Integrate multi-discipline efforts
• Inculcate a shared vision and understanding
• Data is a ‘thing’ – vital infrastructure element foundation of the n-tier architecture
• Not a project, more than a program…it’s part of the core foundation

There is no question about it – the foundational levels of people, process, governance and technology required to establish data management on a  sustainable basis are coming together under the CDO umbrella.

What does a Chief Data Officer (CDO) do?

Data/BI/Analytics Evolution @ NetFlix

More data + Better models + More accurate metrics + Better approaches & architectures = Lots of room for improvement!

It’s amazing to watch how quickly the data engineering / analytics/ reporting/ modeling/ visualization toolset is evolving in the BI ecosytem.

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