The high volume of decisions a company must make, their complexity and direct impact on customer experience and the profitability of the business, make automated, real-time decision-making necessary. As we already mentioned in our article “Applying Artificial Intelligence to decision-making (spanish),” Digital Decisioning is the field that deals with business decision-making, using and integrating different Artificial Intelligence techniques. It uses decision management to deliver business value through AI, making use of business rules to ensure agility, transparency and compliance, and seeking learning and continuous improvement. This way, it achieves accurate, consistent decisions in real time in every contact point with the customer.
Predictive Analytics, optimization and business rules (BRMS)
Digital Decisioning is based on 4 main principles: the automation of decisions; prediction; transparency and agility; and continuous improvement. These abilities are achieved through the implementation and integration of different technologies.
To improve prediction and accuracy in decision-making, Digital Decisioning applies analytical techniques, such as Machine Learning and mathematical optimization. This way, it can make decisions based on data, anticipating future events in a more accurate and customized way.
On the other hand, it uses business rules systems to ensure that policies are correctly applied across different channels and business processes over time. Additionally, this technology gives it greater transparency. It allows it to provide a quick response to market, regulatory or company changes, thus reducing the loss of opportunities and the time and cost of making changes to business rules. Rule management systems have simulation capabilities. Therefore, the company can test the impact a change in rules will have before bringing it into production environments.
Benefits of implementing Digital Decisioning
Using Digital Decisioning in a company can help tremendously in different business areas. By integrating different technologies and centralizing all decision-making in the company, room for improvement is very high. Some of the main benefits of its implementation are:
- It centralizes and standardizes decision-making in the company.
- It automates the process by obtaining accurate, consistent, real-time decisions.
- It Improves predictability and accuracy in decision-making.
- It ensures agility, transparency and compliance at all times.
- It provides a quick response to market, regulatory, or company changes.
- It reduces the loss of opportunities and the time and cost of making changes to business rules.
- It is accessible to non-technical professionals.
- It allows you to test the impact a change in rules will have before bringing it into production environments.
- It increases performance and customer satisfaction.
- It reduces the costs of manual decision-making.
- It provides added value through its continuous improvement approach.
With Digital Decisioning, organizations can improve the quality of their decision-making over time.
By combining predictive analytics with business rules and simulation capabilities in a single system, Digital Decisioning allows business users to ensure the best possible outcome by defining and running simulations and adjusting parameters for different scenarios. This way, they can then quickly modify the rules and immediately see the deployed changes, having the flexibility to make adjustments as the needs of the business change.
Let’s take an example of a typical Bank’s Credit Scoring model. A specific entity decides to carry out a predictive model to know whether to grant microcredits to its customers. In this case, the entity or the business manager has two options:
- To delegate decision-making to a predictive model, with its corresponding error rate. It should be noted that the goal of a predictive model is to generalize on past data, to make a new decision on new data, which it has never seen.
- To combine different Decision-Making fields, that is, to use Machine Learning algorithms that allow discovering clear patterns of behavior and include them in a Business Rule Management System to be able to manage the decisions.
If they choose the first option, the decision will be delegated to a black-box algorithm. Therefore, if new data that the model has not been able to generalize in its training appear, it will be classifying it as OK when it may be a KO. In this case, they will have to wait until they have new data to retrain the algorithm and have it re-generalize on the new data. In the meantime, the entity or business has no way to control the decisions.
If they choose the second option, they will rely on Machine Learning algorithms (C4.5, Association Rules, RIPPER, Random Forest, or other variants) to obtain patterns. These patterns will lead them to a BRMS in order to have control over the decisions. This way, they can change them at any time, and, above all, they can find out why a customer has been subjected to a particular decision.
As a further example, we have a data set where we have to predict the final variable, that is, if the customer is going to be a good payer or not.
Since our customer has chosen the second option, they have decided to take advantage of Machine Learning techniques. In this case, they have used the JRip algorithm, which has allowed them to discover thirteen customer patterns with an accuracy of 73.3%.
Taking this information to a system of rules allows them to have control over the decisions that are made and be able to change them by specific business criteria according to the needs of the Market or the Organization.
In the example we are dealing with, we see the 1 to 1 relationship between the rule discovered by the ML model:
(checking_status = Medium Income) and (duration >= 24) and (savings_status = Low) and (residence_since >= 2) and (credit_amount <= 5234) and (credit_amount >= 2718) => class=bad
And its easy implementation into a Business Rule Management System:
This system will allow business users to change, add, or modify any decision in real time.
To sums things up, in this example, we have seen how two fields of Decision Making coexist to set up a Decision Management and Digital Decisioning System and to be able to automate them.
More than 12 years of experience in Digital Decisioning
At decide4AI we are experts in Predictive Analytics (Machine Learning), Mathematical Optimization, and Decision Management. We have been developing and implementing customized solutions that integrate all the technologies needed to achieve the best possible results and generate value for our customers for over 12 years.
Therefore, we have the knowledge and experience needed to ensure the success of a Digital Decisioning solution where you can centralize, optimize, and automate all your company’s decision-making processes.
We also have many success stories in different industries and business areas, from insurance companies and banks to logistics, retail, and production companies.
If you want to know more about decide4AI or our work in the insurance sector, follow us on Linkedin, Twitter and Youtube.