2-day A.I. Workshop for Executives in Financial Services 2019

In this workshop, led by world-renowned experts, we will explore practical considerations when building artificial intelligence (AI) and machine learning (ML) solutions for financial institutions. We will provide you with a powerful frameworks and tools to become an expert in your organization.

During the rigorous hands-on exercises, we will internalize best-in-class tools and templates for implementation, maintenance and the buy-in of the your peers and the stakeholders for the artificial intelligence solutions of your choice.

We will also learn how to structurally solve complex legal and compliance challenges arising due to the use of automated decision making systems and artificial intelligence systems.

We will cover:

1. Sticky, insightful and simple ways of explaining to others the conceptual, mathematical and legal contrasts between the top ML methods: logistic regression, gradient boosting and neural networks and the performance metrics behind them

2. The 'holy-grail' of machine-learning: the self-learning, self-executing and self-deploying models: do they exist at all and, if yes, how to maintain them?

3. When you can build 'quick & dirty' and when you need 'slow & clean' model and why it matters - e.g., why mindlessly focusing on the final ROC value is not good and looking at the ROC curve shape function is often equally important

4. How to turn-off your autopilot of mindlessly optimizing your models on the standard performance metrics (AUCs, GINIs, loss rate, R2, etc.,), how you can cheat them, why understanding confusion matrix and sensitivity/accuracy/specificity/F1-score is more important and why proper validation design through out-of-time/of-sample tests is a must

5. Fitting the IT architecture and platforms (databases, software, tools) into the scope of your AI/ML activities and understanding when Excel is enough (you knew that you can run a neural network in Excel, right?)

​6. How to ensure that your model is optimized for KPIs you understand, (customer-life-time value, cross/up-sell, churn, expected loss, etc.) and not for some typical data science mumbo jambo mathematical constructs which are often three-degrees removed from your quarterly business targets

7. Getting stakeholders buy-in and board/steerco approvals through sculpting a compelling financial impact analysis for a final go/no-go decision:

- how to estimate the marginal added value of your AI/ML solution over other competing solutions through backward- and forward-looking what-if-and the NPV (net present value)- scenarios

- modelling across number of scenarios such as with vs. without historic financial data (existing/innovative solution) or record- vs. portfolio- level estimations (slow/fast execution)

8. Deciding between: in-house developments, hiring new staff, in/outsourcing, buying software, partnering up, creating joint ventures, and acquiring start-ups and hence:

- covering possible implementation scenarios on - existing infrastructure, micro-services, internal/external cloud, SaaS, etc.

-understanding legal remedies and warranties/guaranties on commercial vs. open-source software

9. Best-in-the class practices of end-to-end (E2E) cycles of building, deploying and maintaining robust ML models across your organizations: why everyone wants always to build the models but none ever wants to implement and maintain them

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10. Why most pilots and POCs become final solutions and how to stagger and phase them correctly at your own organization or on-site at clients

11. De-biasing algorithms and making them fair again: managing the model's likely discriminatory misjudgments, about such sensitive user-characteristics as gender, ethnicity, religion or age, which mainly stem from the ill-thought mathematical assumptions and lack of explicit rules for tackling model's hidden correlations​

12. How to explain your model's decisions in a compelling way to the end-users in a compliant manner but without revealing the inner workings of the model and what the current legislation actually mandates so you don't throw the baby out with the bathwater

13. Who is end-responsible for the data processing - you or the party you engage? The crucial difference between the data controller, data processor and how to identify them. Is possible to indemnify oneself against GDPR fines by holding accountable the data processor or data controller, can you split the fine or buy an insurance against it? 

14. Methods of anonymizing data

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15. Is it worth processing biometric or sensitive personal data