Algorithm

[Vishwadeep Khatri 264]

Q. 186  Provide some of the latest examples where breakthroughs with improved accuracy or reduced turnaround times have been achieved due to use of effective algorithms for predictive analytics. Do provide your reference source of information at the end of your response.

 

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[Indrani Poddar 1]

An algorithm is a process or set of rules to be followed in calculations or step by step problem-solving methodology followed.

 

In our organisation we have built algorithms using Random Forest model on R, wherein the quality check process has significantly improved efficiency and accuracy of the process. Typically in a quality check process either samples are randomly picked without any logic or rules being or rules defined are typically; all employees with less than 6 months experience 100% audit to be done, critical process 100% audit, 100% audit of all transactions above a certain threshold value, 50% audit between a given value and another so on and so forth. This conventional audit process uses maximum effort and cannot ensure high coverage of errors. What we have done is basis past trend of errors for 1.5 years, identified critical variables and combination of errors and developed an algorithm that gives the list of transactions for the audit team to audit which has highest potential of error occurrence.  The model developed provides the audit team the transactions to be audited from the total number of transactions processed.  Thereby optimizes effort spent and has lead accuracy rate closer to 100% using this predictive analytics. This algorithm also has self learning capability with any new type of error occurrence. 

 

Another example where we have effectively used algorithm as predictive analytics again used Random Forest Model was to predict the number of emails that can be responded in first attempt within a shorter turn around time. In a query ticketing process we get queries from customers in a ticketing tool inquiring about invoice payment status, payment not received, deduction amount, disputed payments etc. The inflow of such emails is about 250 per day with an SLA of 3 days to be responded or resolved. The current process was facing extreme challenges with backlog building up on the queries to be handled leading to miss of SLA and high vendor and customer dissatisfaction. Also simpler queries were pending for a longer turn around time as the team was handling the queries one by one basis ageing. We developed an algorithm using past history of emails / queries closed to predict which queries can be resolved in first attempt and which queries would need more research and hence passed on to the resolution team. This has helped improve current TAT of emails being resolved, higher First Pass Yield,  reduction of backlog of emails without adding additional resource and team is having more qualitative time for research of complex queries or doing effective follow up with dependencies on customer organisation.

[Sreyash Sangam 2]

Below are certain examples of scenarios wherein the organisations have deployed various AI and digital algorithms to improve the  business performance.

 

example 01 :One of the Financial transaction company called Paypall had collaborated with Rapid Miner to gauge the intentions of Top customers and monitor their complaints. The digital analytics team of Rapidminer helped Paypall to manage and analyze the huge amount of data. AI and data science engineers of Rapid Miner used SENTIMENT ANALYSIS in different languages based on the various social media posts and messages. Rapidminer also find most iterative word or repetitive word to identify the common area for concern by the customers. After various such analysis Paypal was able to categorize the customers into Top Promoters and Top detractors. This helped in improving the customer satisfaction as well as the Total Turnaround Time(TAT).

 

Example 02: Dataiku is a Newyork headquartered company that offers Dataiku DSS (Digi science studio). It helps the customers of vivid range involving transportation, logistics, using raw data to predict which component is going to fail and at what interval. Using the vehicle sensor data, it can be used to give the predictive solution of what and how frequently the parts are going to fail the frequency for Preventive maintenance. This digital tool and predictive analysis helps reducing the downtime.

 

Example 03: The bank of America, one of the customer of a digital technology company DataRobot. BoA might lend money to customers in the form credit,  loans, assets etc. Growing their business means giving more credits to the clients. The Data Robot enabled AI and data analytics is used to predict which particular clients or customers can get defaulted based on the raw data and historical performance. This is done by assessing the Loan repayment data and social media platforms. Also this digital tool can help in identifying the default rates of new customers identifying the rate of default by existing customers.

 

Example 04 : One of the AI and diigital platform led company, Apixo can use HCC Profiler to predict the risk of risk plans they list on health insurance exchanges. The software could potentially analyze data from patient records and medical billing

 statement to provide health insurers listing certain plans with an overall risk score of such plans.

 

One of the main source for these case studies:

https://emerj.com

[Rupinder N 31]

The chosen best answer is that of Indrani Poddar for the detail provided in the algorithm used. Sreyash's answer provides additional examples and understand the concept better.