My focus on business intelligence the last few years, my long term interest in artificial intelligence and the growth of machine learning came together to drive the content for my latest Forbes article.
In my work with TIRIAS Research, I’m covering machine learning. As part of that, I am publishing articles on Forbes. One thing I’ve started this month, with two articles, is a thread on management AI. The purpose is to take specific parts of AI and machine learning that are often described very technically, and present them in a way that management can understand what they are and, more importantly, why they provide value to decision making.
VentureBeat hosted a webinar that missed the mark in the title, but is still worth a watch for those interested in how technology is changing the banking industry. Artificial intelligence (AI) was only discussed a few times, but the overarching discussion of the relationship between the young financial technology (fintech) companies and the existing banking infrastructure was of great value.
The speakers were:
- Katy Gibson, VP of Application Products, Envestnet | Yodlee
- Dion F. Lisle, VP Head of FinTech, Capgemini America Inc.
- John Vars, Chief Product Officer, Varo Money
- Keith Armstrong, Co-founder and Chief Operating Officer, abe.ai
- Stewart Rogers, Director of Marketing Technology, VentureBeat Sponsored by Yodlee
The opening question was about the relationship between fintech and banking organizations. The general response was that the current maturity of fintech means that most companies are focusing on one or two specific products or services, while banks are the broad spectrum organizations who will leverage that to provide the solutions to customers. Katy Gibson did point out that while Yodlee does focus on B2B, other fintech companies are trying to go B2C and we’ll have to see how that works out. Dion Lisle suggests that he sees the industry maturing for the next 18-24 months, then expects to see mergers and acquisitions start to consolidate the two types of businesses.
One of the few AI questions, one on how it will be incorporated, brought a clear response from Ms. Gibson. Just as other companies have begun to realize as machine learning and other AI applications begin to be operationalized, clean data is just as important as it always has been. She points out that banking information comes from multiple sources, isn’t clean and is noisy. Organizations are going to have to spend a lot of time and planning to ensure that the systems will be able to be fed useable information that provides accurate insight.
There was an interesting AI-adjacent question, one where I’m not sure I agree with the panelists. Imagine a consumer at home, querying Alexa, Siri, or other AI voice system and asking a financial question, one such as whether or not personal financial systems are good to buy a specific item. If the answer that comes back is wrong, who will the consumer blame?
The panelist consensus seems to be that they will blame the financial institution. I’m not so sure. Most people are direct. They blame the person (or voice system) in front of them. That’s one reason why customer support call centers have high turnover. The manufacturing system might be to blame for a product failure, but it’s the person on the other end of the line who receives the anger. The home AI companies will need to work with all the service providers, not just in fintech, to ensure not just legal agreements specify responsibility, but that also the voice response reflects the appropriate agreements.
The final item I’ll discuss was a key AI issue. The example discussed was a hypothetical where training figured out that blue eyed people default on home loans more often. What are the legal ramifications of such analysis. I think it was Dion (apologies if it was someone else), pointed out the key statistical statement about correlation not meaning causality. It’s one thing to recognize a relationship, it’s another to assume one thing causes another.
Katy Gibson went further into the AI side and pointed out that fintech requires supervised learning in the training of machine systems. It’s not just the pure correlation/causality issues that matter. Legal requirements specify anti-discrimination measures. That means that unsupervised learning is not just finding false links, it could be finding illegal ones. Supervised learning means data sets including valid and invalid results must be used to ensure the system is trained for the real world.
There were more topics discussed, including an important one about who owns privacy, but they weren’t related to AI.
It was an interested webinar with my usual complaint about large panels: There were too many people for the short time. All of these folks were interesting, but smaller groups and a more tightly focused discussion would have better served the audience.
Cloudera held a pretty impressive web event this morning. It was a mini-conference, with keynotes, some breakout tracks and even a small vendor area. The event was called Cloudera Now, and the link is the registration one. I’ll update it if they change once it’s VOD.
The primary purpose was to present Cloudera as the company for data support in the rapidly growing field of Machine Learning (ML). Given the state of the industry, I’ll say it was a success.
As someone who has an MS focused on artificial intelligence (ancient times…) and has kept up with it, there were holes, but the presentations I watched did set the picture for people who are now hearing about it as a growing topic.
The cleanest overview was a keynote presentation by Tom Davenport, Professor of IT and Management, Babson College. That’s worth registering for those who want to get a well presented overview.
Right after that, he and Amy O’Conner, Big Data Evangelist at Cloudera, had a small session that was interesting. On the amusing side, I like how people are finally beginning to admit that, as Amy mentioned, that the data scientist might not be defined as just one person. I’ll make a fun “I told you so” comment by pointing to an article I published more than three years ago: The Myth of the Data Scientist.
After the keynotes, there were three session of presentations, each with three seminars from which to choose. The three I attended were just ok, as they all dove too quickly into the product pitches. Given the higher level context of the whole event, I would have liked them all to spent more time discussing the market and concepts far longer, and then had much briefer pitch towards the end of the presentations. In addition, they seem too addicted to the word “legacy,” without some of them really knowing what that meant or even, in one case, getting it right.
However, those were minor problems given what Cloudera attempted. For those business people interested in hearing about the growing intersection between data, analytics, and machine learning, go to Cloudera’s site and take some time to check out Cloudera Now.