Loan Origination:

Providing access to timely credit and finance is one of the key aspects of any banking institution. A typical loan origination covers various aspects from reach out to pre-qualification, application, underwriting, credit decisioning, quality control to funding. End-to-End Digital transformation can help banks effectively use it as a point of differentiation and that of competitive advantage in a fast changing digital economy.

1. Use Analytics to target and find ideal customer, and map out their journey, understand the key touch-points, moments of truth for your customers.
2. Corroborate your identified customer journey as well as simulate and explore potential new journeys that might be optimal, with the help of Process Mining.
3. Monitor your current origination SLAs using actual data, throughput times via Process Mining.
4. Use intelligent automation to convert paper-based forms into digital format and expedite the process with straight through processing, and to validate against external systems for e.g. sanctions databases, credit bureau info etc.
5. Automate your underwriting criteria using decision management rules.
6. Find deviations and non-compliances in your process by using process mining to compare and contrast against your documented processes.
7. Make your process learn over time by leverage your already developed machine learning models and using them in conjunction with  your process mining tool.

Process of Care Measures for Healthcare Providers:

Process Measures are evidence based quality measures that represent how well provider facilities( hospitals, nursing homes etc) impart care to patients. Measures are based on scientific evidence and can reflect guidelines, standards of care, or practice parameters.Facilities use data from patient records to derive a rate or percentage that is used to measure performance.

Often these performance measures are linked to impacting an outcome metric, for example LOS-length of stay or re-admissions rate or mortality rates etc, which are reported to CMS and other government bodies and payer organizations and may impact Healthcare Providers commercial contracts. An end-to-end digital transformation approach can help provider organizations to effectively monitor, track and improve these performance measures.

1. Use Intelligent Automation to capture data from medical imaging systems, lab tests, prescriptions to formalize and create a knowledge base of data on new and past patients where possible.
2. Merge this formalized knowledge-base with third-party patient generated health -data( apple, android devices for example) and inpatient data from internal EHR systems to create an extended event log in your process mining tool.
3. Analyse the extended event logs for identification of optimal clinical pathways and monitoring of important KPIs like wait-times, utilization info of provider resources like ERs, OTs, ICUs etc.
4. Dig deeper into finding specific root-causes for delays/extensions in cases where LOS(length of Stay) exceeds defined thresholds, using Process Mining.
5. Leverage Analytics for specific models for simulation building and performing further validations specific to chosen Process of Care.

Lead to Order:

Amidst declining ARPU’s Telecom companies are increasingly looking to effectively market new revenue streams, products and services to new prospects and existing customers. Lead to Order is the set of business processes involved with acquiring the lead, converting to an opportunity, creating and securing the quote, receiving and fulfilling customer requests for goods and services. End-to-End Digital Transformation of Lead to Order is key to expanding revenues.

1. Leverage the data around customer profile, demographic, usage, network and location to create unique customer segmentation for traget messaging using analytics tools.
2. Use intelligent automation and task mining capabilites to digitalize the order management experience for customers and employees.
3. Analyse the current lead to order process by connecting your CRM data, ERP, Order Mgmt and other systems. Gain visibility into key KPIs like Order placement by channel, Cost per order, fuflfillment accuracy rate, rate of return, order fulfilment cycle time, on-time shipment rate etc.
4. Identify improvement opportunities impacting key KPIs using Process Mining.
5. Use Intelligent Automation to improve customer communication around order fulfilment.

Predictive Asset Maintenance:

In utilities asset maintenance is key as machinary breakdown can be very expensive and time consuming, resulting in significant revenue loss. End to End digital transformation can help utilities leverage a combination of technologies to avoid a potential loss.

1. Stream the data from sensors attached to production systems, and other maintenance management ystems likes CMMS, ERP, MES  into process mining to get a complete view of process and assess & monitor performance KPIs, benchmarks like MTBF, MTTR, MTTF, MDT. 
2. Use analytical machine learning models built to monitor failure thresholds, in conjunction with Process Mining tool to predict outages.
3. Digitalize historical data from paper based work-orders and other administrative processes  using intelligent automation and used for further analysis.
4. Improve the efficiency of your maintenance team by using the process mining simulation and exploration techniques to compare and contrast process paths to find an optimal re-engineered process path with shortest time for work order execution.
5. Identify best performing suppliers and vendors for your part procurement and inventory by using Process Mining to analyze data across a range of parameters from delivery time to cost to quality.