How does AI improve risk management?
Tags: Artificalintelligence, Data Science, Risk Management, Data

It would be expected that exploring the data capital, as the sum of every bit of information about company activity, prevents from making elementary mistakes in decision making. Common thought is that more data yields better results in decision making by accessing additional information to optimize company business. Today’s fact is that data capital increases exponentially necessitating additional investments to proceed this “raw” capital for sake of the business. An optimization by means of integrating any useful data depends notably on the quantity and the quality of the observations extracted from the raw data.

AI for Risk Management(RM)

It is unrealistic to ignore or deny the connection between this uprising data driven business activity and Enterprise Risk Management (ERM). However, having better risk identification, evaluation, prediction and/or forecasting require not only to get enough information on the state of the nature, but also to use the adequate technology to proceed this information.

The opportunities proposed by AI implementation within ERM have inevitably been discussed and criticized by risk managers. Their major concerns were the limits and the additional risks arise from sensitive data exploration by AI. As one of the leading conferences organized in Europe, by FERMA in 2019 at Berlin, AI was introduced to risk community such new scope of perception of risk handling through data exploration. The white paper on “Artificial Intelligence applied to Risk Management” by FERMA shares the same vision with European Commission’s approach on managing AI-related risks in Europe. Further a risk conference organized by “Association pour le Management des Risques et des Assurances de l’Entreprise” (AMRAE) held in February 2020 at Deauville-France, helps companies to share their concrete use-cases of AI solutions with the risk audience. Aim was to show multiple real case implementations of AI tools within insurance sector and ERM. The rising curiosity of risk managers on AI has lately been discussed at ERMINe Risk Talk in December 2020. The talk was dedicated to explain the basic concepts of AI, to present potential ways of AI implementations and to precise the limits with condition of usage of AI as a tool for risk managers.

Two groups of questions of interest that are raised at these conferences:

· How should AI help ERM?

The questions are dedicated to comprehend of the methodological aspects to understand the limitations and opportunities proposed by AI .

· How could AI improve risk management activity?

The questions are about clarifying the integration of AI into ERM to get improvements over risk identification and evaluation.

Some points to improve ERM

Three pillars of implementation of AI within RM is:

1. Helping to reduce time burden in data collection, preparation and analysis.

2. Helping to get more representative data yielding better results with higher accuracy in risk analysis.

3. Helping to optimize interpretation of the information:

3.1. by creating better metrics and specific KPI’s for given risks

3.2. by identifying under estimated risks for given indicators and patterns

1 and 2 explain how AI should help ERM in way of increasing performance by furnishing advanced tools to collect and analyze data in RM decision making. 3 (including 3.1 and 3.2) is notably helps improving the risk identification and evaluation process which, in turn, may change the perimeter of RM activity.

All is about knowing whether need more data improve RM activity or not

To answer this question, two issues must be considered. First is about data used in RM. Second is about the methods used on the risk data. There are several issues surrounding the first point. Keeping 3V of Big Data (velocity, veracity, volume), data used by RM are not really enters into Big Data ecosystem. It’s for sure, the business sector and the risk universe defined for the activity determine the form and amount of data. In most case, RM don’t really use or have desire to control complex or unstructured data in their analysis due to time constraints and to lacking of advanced techs to procced these information. Therefore, even if the risk data is relatively small for an advanced analysis, it doesn’t prevent implementation of the methods proposed by AI since it still allows to get new information from existing data which bring us to consider our first aforementioned issue.

The fact is that, RM often use labeled data in their analysis. Structured numerical info mostly saved in excel files. As an ultimate methodology followed by RM, once the observations about similar past events exists, this historical information helps to compute the probabilities about these already enregistered events at any moment. Therefore, there are several issues encountered in this methodology used by RM.

i. First problem concerns the errors due to subjectivity in labeling risk registers. For a given any definition of risk, RM use rule-of-thumb method for determining its value.

ii. Second problem is about manual sampling, reporting and validating of risk scores by RM. For certain cases, there may exist a probability to mislabeling during risk registers.

iii. Third problem concerns lacking of sufficient observation causing lower quality and quantity about observations. Even for the similar risk categories, there could be huge differences in the number of observations for each risk.

Those above mentioned points would cause lower accuracy and weak comparability within the risk analysis. Depending on the activity area, most of the RM don’t have tools or the methods to test the reliability and validity of their results. In their analysis, RM mostly consider generative joint probability distribution methodology. As a consequence, they are limited in terms of testing the conditional risk variability in their analysis. Another limitation is that the risk results must necessarily fit with previous registered observations. The implication is that RM can barely analyze, identify or follow the events and causes yielding an unknown risk (e.g. Pandemic situation) since their analyses are conditional on initially registered data of the described features. In other terms, there is no open gate to interpret or to investigate the causality hidden behind the residual risk values measured from their model, hence remains in Blackbox.

Be that as it may, it is reasonable to expect that integration of AI methodology helps to eliminate most of these issues.

There are two detected constraints against AI implementation within ERM.

i. Data type and size

ii. Limited comparability between risk registers.

Integrating different data types will helps to increase the size of the data and to improve the accuracy of the result by implementing advanced statistical and mathematical methods used by data scientists. Risk comparability enables to create new measurement methods for risk analysis.

Integrating data

Numerical unlabeled data: Collecting and analyzing the data is time consuming and highly biased depending on subjectivity. As an alternative strategy , RM could increase the number of observations by injecting new unlabeled similar observations into already existing data. Analyzing and learning risk from non-labeled data refers to unsupervised learning. Unsupervised data helps to follow inner clusters from similar and unusual patterns hidden within the data. Once the clustering is objectively done, RM can use for the enrichment for their job. However, unsupervised data driven RM analysis has knife-edge conditions meaning that the amount of data for given risk analysis could still be insufficient due to context mismatching.

Non-numerical data: RM are traditionally limited by using numerical data in their analysis. The content of risk data could be increased by accessing complementary information to get more sustainable results. Integration of non-numerical data into risk data would expect to increase the predictability of happened and unhappened events. The additional data may include texts, images, sounds as well as videos. Today, RM don’t really know how to access, to collect and to explore these additional data.

Increasing comparability

By its nature, risk analysis has an temporal aspects meaning that it is impossible to concretize any risk whenever the factors causing undesired event are isolated from their past and future. As a methodology, present value is obtained by projecting of risky events on past events. Such an approach followed by RM is limited due to ignoring conditional interaction between risks and to lack of theoretical comparability methods among similar past events of different risk registers. In other words, for a given domain processes of two different risk category may share certain similar characteristics but their method doesn’t allow to track their conditional dependency. An example is that “the breakdown of a machine” and “disruption of activity due to internal fire” may both have similar temporal influence on the company business (i.e. immediate exposure) and may further be caused by similar internal factors. Enrichment of risk data with introducing similar and not similar factors enables to bring these risks into a common unit values which, in turn, allows RM to analyze common past and future events both by controlling causality and correlation through these shared factors.

Conclusion

1/ Today, Risk Managers (RM) are curious about potential implementation of Artificial Intelligence (AI) for their Enterprise Risk Management (ERM). Their concern is the enrichment of risk data through identification, preparation and creation more comprehensive evaluation metrics for analyzing ongoing company risk process in the way of increasing the perimeter and quality of company’s’ RM activity. Giving an opportunity to RM in implementation of data science methodology (i.e. implementing machine learning methods to get less unbiased measurement for identification, valuation of risk) will opens up new avenues.

2/ The companies have the data capital on their business activity. RM must adapt and improve their risk analyze and they must change their data model to better explore the quantity and quality of available information extracted from the raw data. As the organization grows, more product and service provide still more data than before. RM must follow this growth and must get the right tools to project these data into in ERM analysis. AI can mainly be used for understanding the patterns hiding behind the data or further to organize, capture and identify the valuable information in order to better optimize any processes of Risk. Here, we can’t really talk directly about costs of decision! Mostly AI provides new or ignored information in more effective way by increasing the quality of Risk Managers decision. The heard of AI implementation is having enough sample data. RM must increase the dimensions of their risk data by new types of data and further try to adapt unsupervised learning methods into their analysis. For further advanced statistical analysis, RM must find a way to compare risks defined within their risk universe.