When AI-driven decisions are acceptable to clients ànd Risk & Compliance

By Data science, News

Artificial Intelligence (AI) algorithms are commonly used in many sectors. Yet, many financial services companies still apply AI very limited. Especially for those decisions that could very significantly impact the business results. Uncertainty about the acceptance of its uses by internal and external stakeholders is a main reason. What is this uncertainty and what handles can help? We offer a client-centric framework for such handles and illustrate these with one of our projects. The framework is composed from the perspective of the end customer, but also helps specifically to meet internal stakeholders as well.

Potential for more value with less work
Specialists like underwriters, risk experts and insurance claim handlers spent a lot of time on repetitive assessments and decisions. When these points in the business process can be automated the specialists can focus on more challenging activities and provide the clients with much added value. Often rules-based decision models are composed first, but these have their limitations. From experience we know that decision models augmented with AI can further increase the straight-through-processing ratio and make the process more accurate and consistent.

Two parties are cause for uncertainty; clients and Risk & Compliance
Those AI models that can most contribute to business results can also potentially most impact clients. An AI model that wrongly assesses credit worthiness or insurance risk can of course cause much more harm than a model that wrongly assesses online click behavior or cross-sell opportunity. These high-stakes models are therefore very much in the attention of several internal and external stakeholders such as; regulators, risk officers, clients and client representatives.

Because of this Risk & Compliance teams often place strict demands on model validation and monitoring. The more complex and more impactful the model, the more difficult it is to meet those demands. Many organisations also do not have much experience with this. Next to that, financial services providers are uncertain whether their clients and intermediaries will accept the outcomes of these strongly differentiating, high-impact models. These uncertainties in both front office and back office result in resistance and slow down innovation.

Client acceptance as the leading perspective
The framework below provides guidance to get AI accepted by both internal and external stakeholders. It is composed from the client perspective, so it appeals more and provides more energy compared to only thinking in terms of rules and obligations.

In short it comes down to this: As a client you want to understand how a (automated) decision was reached; you want to feel it’s a decision you can live with; and you want to be able to work with it. We elaborated on these points.

1.     The outcome must be explainable
You must be told what data was actually used and how that data led to the outcome. But to be really considered as explainable the explanation of the outcome must also be perceived as ‘logical’. In other words, the explanations must align to our understanding of how the world works.
Of course, the model must also be consistent, such that outcomes and explanations for similar cases do not differ much.

2.     The explanation must be acceptable
The data used must not be perceived a violation of privacy and so too for the insights obtained from it (using a predicted possible pregnancy, or upcoming company ownership transfer, deducted from a change in transaction patterns is not appreciated by many clients).
Next to this, the model’s outcomes need to be unbiased towards vulnerable groups, or towards sensitive characteristics such as gender or ethnicity of individuals or in the composition in a labor force.

3.     With a sense of control and benefit for the client
To provide clients with a sense of satisfaction, even in the case of a negative outcome, they must experience a sense of control. This requires the possibility of human intervention in the decision process ànd that the AI model is predictably influenceable. This last requirement allows explaining the client what she can do herself to obtain a better outcome. Of course, it is important that ‘good behavior’ subsequently does indeed lead to better scores and lower interests and rates as a result.

When a decision model can realize the perceptions above, there does not need to be much uncertainty about whether clients will accept the application of AI. Naturally, the service provider must still be able to communicate all of this well enough so that clients are informed complete and in such a way that the above perceptions are indeed effectively transferred.

Killing two birds with “one” stone
Underneath the three client perceptions are quite a few requirements. However, we do have the technology and methodology to realize these. The good news is also that once you realize the three client perceptions you will be able to cover almost anything that Risk & Compliance may need to demand. Because in order to make AI models explainable and acceptable to clients and to have control over the outcomes you must apply specific methods and reach a certain level of control and transparency. With it, you can also meet the Risk & Compliance demands.

So the next time you contemplate a solution to apply AI in high-impact decisions only three questions are relevant: “Can we explain this to the client?”, “Will he/she find it acceptable?” and “Will there be a certain level of control for the client over the outcome?”.

This will reduce much uncertainty and many puzzle pieces will fall into place. The client perspective energizes most professionals more than the necessity of following difficult rules.

We are open to discussing all sorts of matters that involve data science and AI. Especially those that touches upon organisational aspects. Feel free to contact us for a cup of coffee!

Contact
Mando Rotman
E: mando.rotman@igh.com

What Data Science Managers can learn from McDonalds

By Data science, Insurance, News

Insurers and intermediaries digitize their companies more and faster. This has implications for the organizational functions that support this such as the Data Science and AI teams. From our recently conducted Data Science Maturity Quick scan among Dutch Insurers we learn that nearly all companies currently organize their Data Science in the same way, centrally. However, the front runners are now about to transition to a different, hybrid, organizational model. Determining the best organizational model for the Data Science function turns out not to be simple.

How do you organize Data Science and AI as a scalable corporate function, when you can no longer keep it centrally organized and also do not want to switch to a very decentralized, hybrid model?

Three basic models
Actuary expertise and business intelligence have been part of the insurance business for a long time. But since about two decades people with the title Data Scientist started to appear. These professionals usually worked on non-risk use cases, such as in Sales, Marketing & Distribution, Fraud and Customer Service and they wanted to apply the latest, often non-linear, Machine Learning techniques (ML). The introduction of this kind of work often happened very decentralized and scattered throughout the organizations. But as the reputation and expectations of their work grew, these new professionals were often grouped together in central Centers of Competence (CoC).

Fig 1. Three basic models for organizational functions

The CoC model brings some advantages for a new data science function, when compared to the decentralized model. Especially when the company is not yet really functioning like a digital, data driven organization. Five out of six organization in our Maturity Quick Scan have organized their data scientists in a CoC model. However, at some companies, the digital transformation is getting serious and, in that case, strong centralization can result in a capacity bottleneck. Or be the cause of too big of a gap between business and data sience teams in terms of knowledge, priorities and communication.

Switching from a centrally organized model to a more hybrid model is often advised as a best-of-both-worlds solution. This should make it easier to scale up and align knowledge and application of data science closely with the day to day business, while a select number of activities and governance can remain central.

Concerns over hybrid models
Spotify developed a popular hybrid version that has become known as the Spotify model with its Squads, Tribes, Chapters and guilds1. This type of model has become popular among the digital natives and e-commerce companies in this world. But many data science managers in Dutch insurance companies have their concerns about this highly decentralized version of a hybrid model. These concerns are;

  • Data culture and scale of the AI applications may be insufficient still to maintain the needed continuous development and innovation
  • There are often still company-wide use cases yet to be developed that could be realized much more efficiently centrally
  • Next to that, IG&H has identified there is a large gap in data science maturity between corporate insurance and consumer insurance2. A more central organization model can help to narrow that gap

This is why in practice we often choose for a more central version of the hybrid model. But every hybrid (re)organization raises questions such as;

  • How do you keep the complexity of funding and governance down?
  • How do you maintain sufficient control over standards, continuous development and innovation?
  • Which activities and responsibilities do you keep central and which not?

A different perspective; a successful retail business model
Thinking about these questions from a different perspective or from the perspective of a different sector can help to find answers. We do this a lot to solve problems at IG&H. In this article we make an analogy with a good ol’, trusted, business model. In the late fifties of the 20th century fast food giant McDonalds started to conquer the world in rapid pace. The McDonalds concept (branding and methodology) was a proven success, but the explosive, profitable growth was partly made possible by the chosen business model for expansion, the franchise model.

A franchise is a type of business that is operated by an individual(s) known as a franchisee using the trademark, branding and business model of a franchisor. In this business model, there is a legal and commercial relationship between the owner of the company (the franchisor) and the individual (the franchisee). In other words, the franchisee is licensed to use the franchisor’s trade name and operating systems.

In exchange for the rights to use the franchisor’s business model — to sell the product or service and be provided with training, support and operational instructions — the franchisee pays a franchisee fee (known as a royalty) to the franchisor. The franchisee must also sign a contract (franchise agreement) agreeing to operate in accordance with the terms specified in the contract.

A franchise essentially acts as an individual branch of the franchise company.

Fig 2. Illustration of the franchise model
Credits: https://franchisebusinessreview.com/post/franchise-business-model/

The franchise model offers some important benefits to a business concept that help to expand fast and durable;

  • Access to (local) capital
  • Entrepreneurship of local establishments
  • Presence close to local customers
  • Ability to facilitate centrally what locally cannot be realized in a cost-efficient way, or what is an essential part of the success formula (e.g. R&D, Production, Procurement and Marketing)

Each of these qualities can be related to lessons learned and best practices of scaling up Data Science within an organization. As the franchise principle made it possible for McDonalds to rapidly expand into the world, it can also help expand Data Science throughout the company.

Applying franchise model principles
Many insurers desire a more adaptable and entrepreneurial company culture. Ownership, client focus and flexibility are key words. The principle of local entrepreneurship in the franchise model fits very well with this. It encourages both the business responsible as well as the data scientist to work very client-focused and always with a business case to achieve.

Data Science is a broad discipline and it rapidly evolves while it is often not yet embedded well in most companies. This makes a central organizational element very valuable to ensure quality standards, continuous development and innovation. Just like the franchiser in the franchise model often facilitates certain means of production, R&D and best practices. One of the front runners in the Dutch insurance market with respect to organizing their data science is therefore transitioning to franchise-inspired organization model for their data science function.

So, franchise-inspired thinking can be valuable for Data Science organizations that want to move beyond the CoC, but not (yet) want to move to a very decentralized hybrid model like the Spotify model. However, which organizational model and specific choices are the best fit is naturally dependent on multiple variables. Among others; company structure and company culture, the digital maturity of the organization and the maturity of the data science function itself. Because the value of data science and AI are completely dependent on the quality of collaboration with the business and on broad application throughout the company it is vital to objectively assess the current situation and to strike the right balance between centralized and decentralized, with a company-specific touch.

Contact
Mando Rotman
E: mando.rotman@igh.com
Manager Data Science IG&H

Jan-Pieter van der Helm
E: janpieter.vanderhelm@igh.com
Director Financial Services IG&H

1) https://agilescrumgroup.nl/spotify-model/
2) https://www.igh.com/news/page/2/
3) https://franchisebusinessreview.com/post/franchise-business-model/

 

Become a true Data Driven Organization

By Banking, Data science, News

In Commercial Banking it is increasingly important that business processes are digital, data driven and can leverage AI. In the current times of unexpected change we see this magnified. IG&H data scientists observe that organizations who already transformed their processes now truly benefit.

Commercial banks are confronted with a sudden wave of SME client requests, changed risk drivers and changes in risk profiles. Banks want to help and need to figure out what (temporary) policy changes would be meaningful for clients. And also, what the impact of specific changes would be on the bank’s business.

Those who have already transformed their processes are now able to handle this situation much faster and more confidently. Their business processes are already more efficient and more consistent. And in the current time of crisis they also prove to be much more Scalable, Transparent, and Adaptable and they offer more options for looking forward in a smart way.

Scalable
Digital, data driven business processes with a high rate of straight-through-processing and where decisions are made (partly) by AI decision models, require much less human effort. Therefore, they can deal more easily with peaks in workload, especially in times when human capacity may be limited.

This benefit can only fully materialize when there are no bottlenecks in other parts with a crucial dependency. This stresses the fact that individual point solutions are not the way to go. The effective way is a transformation to become a true Data Driven Organization in People, Process, Data and Technology.

Transparent
Monitoring the impact of the current situation on the client experience, on process performance metrics and on KPIs is much more accurate and near real-time in a data driven process. This facilitates communication and coordination throughout the organization and allows management to take more effective actions.

For example: Dashboards can quickly be shared to observe what is really happening. Such as which teams have the highest workload increase. Or where clients’ payment behavior is most impacted.  Analytics can be used to signal early warning indicators such as trends and significant deviations.

Adaptable
AI decision models and business rules can be configured easily to effectuate policy changes like (temporary) higher risk thresholds, lowering the weight of specific risk drivers, higher or lower maximum values, etcetera.

For example: It can be easier to change a few parameters in a risk review decision model, than it is to communicate such changes to whole departments of specialists and coach them to quickly and consistently execute these.

Smart forward looking
Finally, AI decision models can be used to ‘test out’ different scenarios and evaluate very fast the likely effects on individual loans and on portfolio level.

For example: Changing the values of specific risk variables along the lines of different scenarios and observing the predicted effects, is being used to zoom in on those clients who likely require first attention.

AI models can be a very powerful tool to provide insight in likely future outcomes. A data scientist and business specialist who understand how the underlying machine learning works and on what data it was trained can provide a range of quick scan insights within a very short turnaround time.

IG&H’s data scientists and banking consultants continue to work with clients (especially now) to transform commercial banking organizations to remain competitive and benefit from being a true Data Driven Organization.

Would you like to talk about what you can do while your processes are not yet as digital and data driven as you would like? How you can best take the first step? Or how you can leverage your first progress and truly turn the corner to transform into a Data Driven organization? We are ready to help you explore and make data work! Just drop me a note!

Mando Rotman
Manager Data Science IG&H
E: mando.rotman@igh.com