Financial risk management and its data driven solutions have many benefits for companies, but one of the most important one is the significant change of the decision making concept for all of the people involved in the risk decision making chain.
The current system in the commercial real estate and finance business is relying on methods which stayed unchanged for many decades, while the rest of the economy is decisively going through digital transformation processes on all levels.
The situation where we are able to capture huge amounts of data and process them in real time, as well as predictively, is proving itself to be a new business standard in almost every aspect of business operations.
However, talking about financial risk management especially in those industries where low data frequency is present (e.g. market data with commercial real estate) or data identification is an issue (e.g. in case of banks or debt collectors) we are experiencing some sort of stagnation in the development of these kinds of digital tools.
Although companies are investing quite a lot in digitalisation, these efforts are being mainly focused on creating digital data bases and automatisation of some processes, while financial risk management stays out of it for the time being.
Analysing financial risk management procedures with real estate investors for example, it is possible to digitally transform many of the risk procedures, which would on the one hand shorten the risk workflow and therefore the decision making time, and on the other hand would allow to upgrade those processes with even more advanced tools such as predictive analytics.
But for the purpose of this article, let us focus on decision makers and the ways their deciding process could be made simpler, faster and more transparent by introducing data driven financial risk management tools.
BEST & WORST CASE SCENARIOS VS REAL TIME & PREDICTIVE RISK SIMULATIONS
Current financial risk management decision making processes heavily rely on best and worst case scenarios, which are extracted out of the market data from available market reports, offering very vague approximation of the expected risks, often resulting in the subjective assessment and calculations made by a risk or investment manager.
The final decision much too often is based on the subjective opinions of all of the participants in the decision making chain. This happens without a real possibility to objectively countercheck relevant data in real time respectively to prep up management information with data driven short term or long term forecasts.
With a data driven financial risk management system as we understand it, decision makers have the possibility to generate the necessary range of predictive risk simulations in real time backing their decisions with a quantified range of probable risk consequences.
The means of possible applications are huge. It covers tools such as network analytics models to find out what is currently happening in the market - not only in terms of raw market data, but also with respect to uncovering hidden market connections or dependencies among market parameters. All this is done in real time with a perspective of market developments happening in the near future..
These results can be implemented in the data frame and risk management information process of a company in order to get a bigger and deeper picture of current and future developments, be it on the market level or on the company level respectively a combination of both.
SCATTERED & INCOMPLETE OVERVIEW VS ALL DATA IN ONE PLACE
This makes decision making processes much faster, as the whole procedure of reporting changes. Market reports in their traditional sense become obsolete which is also true for best/ worst scenarios and all of the scenarios in between as they can get part of an interactive solution every decision maker has immediate access to while e.g. discussing possible consequences of a project to be invested in.
Data driven financial risk management devotes the same attention to market data, as it does to the integration of this data with the relevant data clusters of companies and and their projects.
Data is regularly updated and refreshed on an automated basis. Hence, not only that the data insights are much deeper and wider, but also those insights allow very advanced analytics options, such as smart predictive analytics combining state of the art statistic tools with machine learning, networks dynamics and similar.
So the whole procedure moves from superficial data, best/ worst case scenarios as well as a bureaucratic, long lasting and protracting manual report production to deep data insights, real time predictive simulations which can be done within minutes. Technical solutions are in place (e.g. cloud-based SaaS applications) in order to give an easy and uncomplicated access to these advantages to all participants in the risk management process, especially to the relevant decision makers.
HANDLING LISTS AND REPORTS VS ONE SINGLE APP
The concept of all of the financial risk data in one place should not be mistaken with the situation of current data bases companies already have, where they store all of their portfolio data which is then implemented in the manual reports.
However, even if the data base is organised and data rich, usually companies have problem with so called "data silos" and data organisation where every digital access or a change asks for a huge IT reorganisation, at least on the project level.
The advantage of the data driven financial risk management system organised in one app is that it is built to extract whatever data is considered to be relevant and to combine it in its algorithms without being affected with the IT system of the company and without being impacted with potential data silos, non-functioning data bases, different data formats, missing or insufficient data and similar...
All of these inconsistencies and troubles are often causing long preparations times of the reports, manual extracting and calculating of the data, need for external market reports with market data and still do not offer deep data insights, such as offered by data driven models.
Well fitted data driven financial risk model offers everything in one place no matter the IT structure behind it.
SUBJECTIVE DECIDING&HIGH PERSONAL RESPONSIBILITY VS OBJECTIVE DECIDING BASIS
One of the positive aspects of introducing data driven financial risk management system, which is about to change the way decisions are being made is availability of objective and deep data analytics basis.
Having deep data insights offers very detailed business intelligence of the project to be decided upon, on highly objective basis that every decision made has in fact firm mathematics behind it as a support.
Of course the decision is still on the decision maker, who has to decide, what is the probability which is acceptable for passing the project forward or rejecting it, but the explanation and grounding of the decision is getting on the completely new level.
This is important when it comes to transparency of the deciding about big projects and investments, especially when there are losses coming out of them due to the changed market circumstances and unexpected events.
Here predictive data driven models can be of big help as they are taking into account improbable events (so called "fat tail risks") as well as the current correlation between market factors and how they impact the future (network dynamics)... However, even if this is not enough and there are unexpected developments - which can always happen - having data driven risk management in place makes it much easier for the decision makers to objectively justify their decisions and therefore achieve higher level of transparency of their decision making process.
(read more about predictive analytics risk modelling on the blog)
Data driven financial risk management introduces many conveniences into the decision making process without any downsides, but maybe the inconvenience to change the system which is being used for many years and decades.
This reluctance to make a change is something lots of companies are facing in many fields, especially now when digital transformation is starting to pick up the speed and it seems that the control over the process is getting weaker.
With Covid - 19 experience many digital transformation processes had to be introduced in order to overcome the physical distance obstacles, but also helped us to easier understand the power of digital tools which are not depending or being hindered by any of the objective circumstances which in one moment succeeded to stop the whole world.
On the other hand, even without the pandemic it is more than clear that the whole world is getting more and more data driven and we have more than ever unlimited possibilities to gather and process data, including predicting the future.
This facts are making companies which understand this and are ready to innovate faster and more competitive, leaving no choice to the other but to follow as fast as they can.