The use of Predictive Analytics and Machine Learning in decision making is widespread. However, streamlining current processes to short-list low-risk transactions and automating them further reduces cost, improves Service Delivery, and reaps inefficiencies. This can be done by Risk-Profile Scoring of the individual cases and comparing them against a predefined benchmark, testing, and finally automating them. The activity can be aligned along with a framework that can be emulated for other varied processes. This ensures enough time at hand for the business to focus on high-risk transactions and high-risk customers.
Enhance Service Delivery with Machine Learning
Machine Learning techniques along with Data Mining and Sentiment Analytics allows finding hidden insights from voluminous and variety of data, which is collected, without any specific programming to look out for any particular source or sequence. Machine Learning has unlimited potential and can be used to improve Service Delivery across all sectors. By aligning it to the following simple framework, the existing Machine Learning dependent processes can be stepped-up to improve Service Delivery to a great extent.
Define the requirements
Find the business problem and define requirements and key variables or attributes that have a high impact and rank them by using statistical methods and by involving Subject Matter Experts. These attributes are required for assigning a risk-score (high risk or low risk) to the application or the customer using the Decision Tree Learning algorithm. Finding the key variables requires pipelines of data for pre-processing, exploring, and analyzing the data. Here Data Mining and Sentiment Analytics add more value while discovering new relationships and attributes.
Be it a customer or a heavy piece of machinery, assign risk-score and shortlist low or no risk profiles. The remaining high-risk subset needs to be focused on personal and resolved manually. This would be the building block for supervised Machine Learning. Towards this end, after defining the requirements as in the earlier step, select the key variables and assign the Risk-Profile Scoring. The high impacting variables can be integrated into the selection process.
Build and evaluate a predictive model
Build and evaluate various models with the help of historical data to come up with the final model. With analytical techniques, select a quality model with a good variable set for assessment. Use three different sets – use the first two sets for training and calibration of the model and the third set for
final testing or model comparison.
Integrate the business rules to automate the low-risk profiles
Combine the Risk-Scores with a benchmark value to convert it into a decision-model by balancing out true-positives, false-positives, and false-negatives (high risk) to find a good trade-off for the given business process in consultation with the stakeholders. The benchmark value selected is chosen by the business after considering the practical factors in detail so that efforts are not wasted on fine-tuning the algorithms later on. Subsequently, automate the low-risk profiles processing and create time for focusing on high-risk cases.
Designing a framework where the business shortlists the high impact variables, assigns risk-profile scoring, and compares with a pre-defined benchmark followed by designing a predictive model and testing and finally automating the straightforward low-risk cases allows the business to focus on high-risk cases. Though this may seem quite a simple process tweak for supervised Machine Learning, just by flipping the otherwise tedious process of high to low-risk profile assessment to the other way round, easily weeds out the simpler cases for automation allowing the business to spend more time on high-risk ones.