What would it mean to you if you closed the decision gap throughout your company, putting your best person, your internal expert, on everybody’s team? - Thomas Mark Keith, ai Innovation Technology
[The Institute for Innovation Development interview series invites innovation experts, innovative business leaders and emerging fintech companies to talk to our readers about their latest innovation activities. The series seeks to learn from innovative business creators, uncover innovation best practices, and discover how to apply these insights into a financial services business model.
At our Atlanta Financial Services Innovation Forum earlier this year, we asked Thomas Mark Keith, Managing Director of ai Innovation Technology LLC, to share with us his role in leading a major new application for machine learning – digitizing expertise. This “right-data” vs. “big-data” technology has been developed by decision scientists in South Africa, and they are beginning to bring this technology to the United States. We asked Mark to share with us their different perspective on artificial intelligence technology and the applications and implications of this new approach for business leaders in the financial services industry.]
Keith: Our artificial intelligence technology is very different than today’s big data approaches to making decisions. Where big data is focused on generating new insight by crunching huge data sets, our technology does something completely different. Our technology focuses on decision makers directly – decoding, digitizing and replicating top decision makers thinking processes. We are not looking for the insight, we are looking at the best decision makers in a company, at the insights they already possess, decoding and digitizing them, and distributing their expertise at scale across an organization.
Value of subjective versus objective decisions
While many important decisions companies and their employees must make are objective, understand that objective decisions by nature do not grant competitive advantage over time as they can equally be arrived at by competitors using similar tools of modeling, with statistics, and by creating algorithms, for example. Ultimately, any advantage that you create in the objective decision making space will be lost.
Contrast that with a firm having an expert whose “expertise” is applied against the most consequential decisions inside your organization. For most firm’s these decisions are going to be the ones that are subjective, otherwise checklists would already be in place, decision trees would be operational, and there would be no need for an expert.
What we know is that, bottom-line, every firm’s success is most dependent on their employees who must make subjective decisions. The goal is to make the best decisions possible. That is why those who consistently make better subjective decisions are considered firm “experts”. Lifting all decision makers in a firm to the quality of an expert is a constant challenge, and this is what our artificial intelligence technology can do quickly.
Our firm is squarely focused on the science of subjective decision making. Subjective decisions are the ones that are uniquely human, and they rely upon the experiences decision makers have over a long period of time. Experience improves the quality, the speed, and the consistency of subjective decisioning. The results that suffer most inside an organization are those impacted by decision error. Subjective decisions are the ones that have proven to be the most difficult to improve, replicate, and scale, for a number of reasons.
By definition, expertise implies someone makes better decisions than most everyone else. To create expertise takes not only baseline knowledge but the judgment, intuition, and a highly developed sense that can only come from many experiences. To reach this level of great decision making in any area requires lots of time and many trials and learnings.
So, the challenge for companies is the time required to develop firm specific expertise. This creates a decision quality gap for the organization between the best and the less experienced. Until now, you could not scale your expert with a precision that would be equal to your expert sitting alongside those making the same subjective decisions. It has been impossible to reliably and repetitively close this decision quality gap.
What would it mean to you if you closed the decision gap throughout your company, putting your best person, your internal expert, on everybody’s team? Companies know the dramatic impact it would have on their results. They are aware of the potential -- they just have not been able to effectively or efficiently capture their experts’ expertise. And it is not from a lack of trying. Expert elicitation efforts have been around for a long time.
Meet TOM – the new tech solution
Here is where our approach has generated the breakthrough. We do not attempt to grind through billions of pieces of data to come up with an answer that approaches a human’s capability to make subjective decisions. We let the human expert do all of the “computing”, and our technology identifies how they organize and weight the variables of their decisions under different circumstances. This decoding process is almost the opposite approach of big data today.
Typically the process takes less than two weeks – from first interaction with the expert to producing a digital virtual expert. Our technology decodes their judgment by interacting directly with the expert, not data. Once decoded, virtual experts can be created as an app, downloaded to your phone or your tablet where you can simply enter the parameters of the current challenge and the virtual expert returns what the expert would say. This technology is called a Tacit Object ModelerSM – we are calling it TOMSM.
Remember that your experts make really good decisions, not 100% perfect decisions 100% of the time, but better than everybody else and at a much higher rate. So, what if TOM can help your other decision makers to be at least as good as your expert for any consequential decision you want to improve? There would be exponential improvement in results that matter for your firm. Improvement can begin literally within a matter of weeks from start to when the virtual expert is in your hands.
Applications for financial services
There are substantial numbers of applications for this technology throughout financial services, wherever expert subjective decisions need to be made. As an example, in a bank, we addressed a serious problem with client retention. We drew from the experts with the best client retention records. How much easier is it to go to the personal banker who is the bank’s most successful at retaining their clients and decode their assessment and strategy for identifying and retaining clients?
Another application that may be of interest is fraud or money laundering detection. Current technologies, with tremendous sophistication, seek out scenarios that appear to match these scenarios. In most cases, exceptions go to humans to investigate and make decisions regarding the right next step. Should it be passed on, does it need further research, or should it be escalated? Our technology can model the best fraud investigators and render expert assessments in real time. Once the experts’ decision making is decoded and digitized, it is ready to be integrated into existing systems and dashboards.
Firms may also choose multiple experts and group these key expert decision-makers from different areas in the firm for different or for similar decisions. This approach creates a virtual expert panel that has all the expertise of all the individuals decoded. All levels of decision makers throughout your firm can have real-time access to this panel 24/7 through existing dashboards or other user defined interfaces.
Simply: TOM globalizes your firm’s best decision making.
Hortz: I want to thank Mark for sharing with us the new applications of his firm’s technology, TOM. There are clearly some early adopter benefits in numerous areas for financial services so I urge you to further explore new technologies like this as the rate of business and cultural change is accelerating. The Institute will be doing a further deeper dive into this new technology conducting a more detailed interview next month. Be on the look-out for it! In the meantime, feel free to contact Mark Keith directly at firstname.lastname@example.org and visit www.aiinnovation.com .
Some applications of this new approach for Financial Services:
- Closing decision quality gap between top 20% sales performers and the other 80%
- Closing expertise gap between retiring experts and younger growing workforce
- Systematically and efficiently replicating the wisdom and expertise of founders and top leaders
This article was previously posted on Financial Advisor Magazine Online.
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