Applications of data science and machine learning in financial services

[A version of this post appears on the O’Reilly Radar.]

The O’Reilly Data Show Podcast: Jike Chong on the many exciting opportunities for data professionals in the U.S. and China.

In this episode of the Data Show, I spoke with Jike Chong, chief data scientist at Acorns, a startup focused on building tools for micro-investing. Chong has extensive experience using analytics and machine learning in financial services, and he has experience building data science teams in the U.S. and in China.

We had a great conversation spanning many topics, including:

  • Potential applications of data science in financial services.
  • The current state of data science in financial services in both the U.S. and China.
  • His experience recruiting, training, and managing data science teams in both the U.S. and China.

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Becoming a machine learning company means investing in foundational technologies

[A version of this post appears on the O’Reilly Radar.]

Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and algorithms.

In this post, I share slides and notes from a keynote I gave at the Strata Data Conference in London earlier this year. I will highlight the results of a recent survey on machine learning adoption, and along the way describe recent trends in data and machine learning (ML) within companies. This is a good time to assess enterprise activities, as there are many indications a number of companies are already beginning to use machine learning. For example, in a July 2018 survey that drew more than 11,000 respondents, we found strong engagement among companies: 51% stated they already had machine learning models in production.

With all the hype around AI, it can be tempting to jump into use cases involving data types with which you aren’t familiar. We found that companies that have successfully adopted machine learning do so either by building on existing data products and services, or by modernizing existing models and algorithms. Here are some typical ways organizations begin using machine learning:

  • Build upon existing analytics use cases: e.g., one can use existing data sources for business intelligence and analytics, and use them in an ML application.
  • Modernize existing applications such as recommenders, search ranking, time series forecasting, etc.
  • Use ML to unlock new data types—e.g., images, audio, video.
  • Tackle completely new use cases and applications.

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How AI and machine learning are improving customer experience

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From data quality to personalization, to customer acquisition and retention, and beyond, AI and ML will shape the customer experience of the future.

By Ben Lorica and Mike Loukides.

What can artificial intelligence (AI) and machine learning (ML) do to improve customer experience? AI and ML already have been intimately involved in online shopping since, well, the beginning of online shopping. You can’t use Amazon or any other shopping service without getting recommendations, which are often personalized based on the vendor’s understanding of your traits: your purchase history, your browsing history, and possibly much more. Amazon and other online businesses would love to invent a digital version of the (possibly mythical) sales person who knows you and your tastes, and can unerringly guide you to products you will enjoy.

Everything begins with better data

To make that vision a reality, we need to start with some heavy lifting on the back end. Who are your customers? Do you really know who they are? All customers leave behind a data trail, but that data trail is a series of fragments, and it’s hard to relate those fragments to each other. If one customer has multiple accounts, can you tell? If a customer has separate accounts for business and personal use, can you link them? And if an organization uses many different names (we remember a presentation in which someone talked of the hundreds of names—literally—that resolved to IBM), can you discover the single organization responsible for them? Customer experience starts with knowing exactly who your customers are and how they’re related. Scrubbing your customer lists to eliminate duplicates is called entity resolution; it used to be the domain of large companies that could afford substantial data teams. We’re now seeing the democratization of entity resolution: there are now startups that provide entity resolution software and services that are appropriate for small to mid-sized organizations.

Once you’ve found out who your customers are, you have to ask how well you know them. Getting a holistic view of a customer’s activities is central to understanding their needs. What data do you have about them, and how do you use it? ML and AI are now being used as tools in data gathering: in processing the data streams that come from sensors, apps, and other sources. Gathering customer data can be intrusive and ethically questionable; as you build your understanding of your customers, make sure you have their consent and that you aren’t compromising their privacy.
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Real-time entity resolution made accessible

[A version of this post appears on the O’Reilly Radar.]

The O’Reilly Data Show Podcast: Jeff Jonas on the evolution of entity resolution technologies.

In this episode of the Data Show, I spoke with Jeff Jonas, CEO, founder and chief scientist of Senzing, a startup focused on making real-time entity resolution technologies broadly accessible. He was previously a fellow and chief scientist of context computing at IBM. Entity resolution (ER) refers to techniques and tools for identifying and linking manifestations of the same entity/object/individual. Ironically, ER itself has many different names (e.g., record linkage, duplicate detection, object consolidation/reconciliation, etc.).

ER is an essential first step in many domains, including marketing (cleaning up databases), law enforcement (background checks and counterterrorism), and financial services and investing. Knowing exactly who your customers are is an important task for security, fraud detection, marketing, and personalization. The proliferation of data sources and services has made ER very challenging in the internet age. In addition, many applications now increasingly require near real-time entity resolution.

We had a great conversation spanning many topics including:
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