1. One of the leading decision science companies approached us with the problem they were facing in monitoring bearing health condition for one of their important industrial clients.
2. They also wanted to find the key components which were causing the flank wear and lag based impact of Acoustic emission and vibration on the rotary table of the milling machine.

1. We used Fast Fourier Transform to break down the bearing signals into its amplitude and frequency and detectedpattern anomalies from these. This helped us in analyzing the health of the bearing.
2. We used Multivariate MARS modelling to find the key components causing Flank wear. Principal factor based root cause analysis along with state-space modelling was employed to find out the time-based relationship between Acoustic emission and vibration.

This analysis helped in reducing machine downtime thereby directly helping the company in saving cost. This also triggered an automatic maintenance schedule thereby reducing unnecessary manpower allocation for machine maintenance.

1.Over- speeding
2.Lane changing
3.Wrong-route tracking
4.Activity monitoring

We are carrying out the following activities:
Object tracking using light intensity. Vehicle classification using height width measurement. Motion detection using optical flow. Anomaly detection using trajectory mapping and band creation.

• Reduction in cost in terms of manual monitoring of traffic violations.
• Automated surveillance and analytics based system which is more efficient and less to prone to human errors.
• Subjective tracking can be designed to eliminate serious threats.

Once the loan is disbursed to the company, it becomes important for the banks/financial institutions to keep a track on the customer w.r.t certain key parameters. With the recent spike in the level of NPA, every bank/FI must become more stringent and vigilant in monitoring the post disbursal metrics/financial behavior of the customer.The entire process of covenant creation and management is currently offline which leads to delayed adherence to triggers. This could represent a significant risk for the bank and its portfolio.

PRM Fincon was entrusted to develop an E2E covenant and trigger management system which would allow for triggers to be created within the application. In addition to creation, monitoring, exception approvals, deferral and waiver process is also expected to be completed within the system.

• The implementation of the Covenants and Triggers system will greatly streamline the tracking process of covenants and triggers and reduce dependency on manual monitoring.
• The rules could be configured according to the portfolio data to monitor any potential stress and can be deployed within a reasonably short time

The client wanted a centralized web based system through which multiple clinics/hospitals can manage their clients/patients visit and through analyzing the visit data able to suggest suitable strategy to engage or retain patients with their clinics/Hospitals. The system should be scalable to handle huge data in/out operation and data processing.

We have developed a web based low-latency system which can support thousands of concurrent connections at a point of time with all the security measures recommended by HIPPA. We also built a custom data processing algorithm to reduce their server cost and time.

With our solution the client now can serve their customers with greater user satisfaction and their server cost is reduced to 60% of the earlier.

Vigilance is mandatory to help ensure compliance. Unless Bank’s controls are bolstered with sound business-systems, Banks will always be playing catch-up. Financial institutions wanted to create a customer risk profile against which they can measure specific transactions as they occur. This customer profile is used as a measuring stick against which transactions can be tested in order to identify specific transactions for in depth examination.

We have analyzed historical transaction history using Data Science and customer profiling based on products, account activities and turnover activities to come up with a rule based solution to identify the characteristics of money laundering transaction using Rule based ensemble and C5.0 algorithm.

This analytics helped to improve the efficiency and effectiveness of transaction surveillance of the institution. By analyzing the population of data, institutions can identify trends and patterns and better determine which behaviors fall outside an acceptable range. On-going analysis has been used to determine correlations and trends between productive and non-productive alerts allowing refinements that better target potentially suspicious activity, reducing overall review efforts.

The national carrier is not only world renowned for providing outstanding passenger journeys, they also offer exemplary airfreight and cargo services tailored to suit the needs of the customers. With a single streamlined solution, the team of cargo professionals with specialised knowledge are committed to ensure transporting goods has never been easier.
Currently the airline’s Cargo booking process is less than optimal.
There is no cancellation fees or disincentive for freight forwarders not to cancel or simply not show up at the last minute. Given the perishable nature of the product (Cargo Space) it is important that the airline accurately capacity control its flights and maximize its revenue earning by minimizing any leakage from last minute no shows.

•Data Diagnostics- A thorough data profiling exercise yielded valuable insights about the pain points affecting the business. Summary statistics on data quality, completeness etc. We also suggested missing value and outlier treatment for a large dataset.
•Analytical Output- Quantitative metrics such as no-show rates, capacity availability and utilization, and opportunity costs were calculated based on understanding that was validated by subject matter experts. Dimensions based analysis across customers, routes and cargo-types was also carried out. We also identified the key drivers which were statistically significant, for these metrics.
•Modelling- We created an overview of the high-level modelling attributes such as cargo-type, customer segment, time of the year, booking rate and opportunity cost. We advised on the choice of models based on the drivers identified and business outcomes expected. This culminated in a dynamic model to estimate the overbooking required based on the unique characteristics of each journey.

Dimensions based analysis and a key drivers’ analysis equipped the various teams to revisit their individual processes with actionable insights. Through a phased approach of process change, behaviour based pricing model and accurate prediction of overbooking capacity, the airline was able to optimise its cargo booking process and minimize any revenue leakage.