Against the backdrop of the changing dynamics of the lending industry, ascribed to the emergence of alternative lenders and innovations adopted by large financial institutions, our client - a global financial services provider found themselves lagging. Saddled with disjointed processes and loosely coupled lending platforms, operational inefficiencies resulting in poor customer experience was holding them back. They sought our help to assess their current state capabilities around commercial underwriting and provide insights around leading industry practices.
As a first step, Infosys conducted workshops and interviews across multiple locations and teams to study the client’s current state - both functional and technical - reviewing capabilities, identifying operational challenges, prioritized severity of business impact, and identifying root causes for these. User personas were adopted to present findings, and provide a compelling picture of reality. This was followed up with impactful learning sessions conducted in association with a leading research firm specializing in the financial services domain. Real-life examples of innovations that the best in the business were bringing toi life were shared. This provided a foundation for the client to start their own transformation journey.
The team then deployed techniques of Design Thinking to uncover the expectations and needs around the underwriting platform of the future. Underwriters and growth consultants were engaged to bring the ‘voice of users’ into the visioning exercise. Customer journeys were mapped to depict the ideal end state. At the end of the 2-day workshop, excited participants armed with over 5000 thoughts on Post-It notes, 286 ideas and 9 prototypes, emerged with a set of viable ready-for-transformation business ideas. Team Infosys went on to provide a set of concrete, actionable recommendations for the way forward. These recommendations for the future state of the client’s commercial underwriting were mapped to concrete initiatives with a clear view into the associated business capabilities and technology architecture.
At the end of 14 weeks of fast paced engagement the client had:
High tech disruptors like Cloud, Big Data, Mobility and Open Source are constantly shifting business models and moving the enterprise goal post. These market disruptions usually come unexpectedly from startups and new entrants, and it is often large enterprises that struggle to adapt and respond. These large enterprises are usually mature organizations that have evolved over the years into rigid structures aimed at delivering reliability at scale. Their workforce has long been incentivized to demonstrate knowledge and depth arrived at via repetition, and their culture emphasizes predictability over reliance on intuition and risk.
However, there is now a growing acceptance that these large organizations require a much needed rebalancing act, necessary for creating and responding to disruptions. There is more openness to discussing concepts such as abductive reasoning, user empathy and rapid prototyping. While there is a growing understanding of why enterprises need Design Thinking, the question still persists about how to enable it at scale, in a mindful way.
The challenge our client sought to solve was exactly that.
Having trained 55,000 business and technology professionals within Infosys, we had a fairly evolved view on the subject, and more importantly, empathy towards our client organization at the onset of this journey. Our program objectives were three-fold:
While this organizational initiative spans numerous quarters, our achievements include:
A heavy equipment manufacturer was looking to improve their dealer and customer experience by providing a portfolio of digital solutions to differentiate their brand’s competitive position. The current approach to delivering digital solutions was slow and did not meet user experience expectations.
Infosys leveraged the IOS mobile platform to define the standards to be able to rapidly produce prototypes as well as production level solutions quickly. We also leveraged a modified Design Thinking approach to quickly take the stakeholders from the initial digital idea to an approved clickable prototype. This approach allowed for accelerated prototyping of any digital solution, even if the solution was not a mobile application. These prototypes allowed for iterative user experience feedback to refine on based on actual user journeys and use cases. Each digital solution was mapped into a broader business architecture and stakeholder journey to ensure that Minimum Viable Product (MVP) solutions could be deployed quickly while the end to end user experience remained seamless and continued to improve with each release.
This factory model, when implemented, is able to take a digital solution from idea to production in as little as 3 months even with the organizational and architectural challenges many large enterprise are constrained with today. When performed at scale the team can be producing one clickable prototype each week.
Unexpected breakdown of equipment (even non-critical) in pharmaceutical manufacturing has severe, multi-dimensional impact. In addition to the costs of down-time and maintenance, there are also potential implications on manufacturing process quality and even drug safety. Given this, a multinational pharmaceutical company was keen to leverage big data technologies and analytic methods to develop a predictive maintenance approach for a broad range of equipment in their plants.
Key goals were to:
To develop, test and arrive at the optimal predictive analytics approach, Infosys focused its knowledge curation efforts on a specific set of equipment – a set of reactors and upstream de-gasifier (cylinders) from one of the pharmaceutical’s plants.
Data extracts for 18 months were analysed to identify major breakdown events to be predicted (the independent variable). Programmable Logic Controller (PLC) system data stored in the database from sensors for pressure, temperature and weights were used as another set of predictors or independent variables. In addition, PLC alarm patterns (such as the count of alarms/hour) were also used as predictors.
Leveraging Infosys Information Platform, a logistic regression (binomial logit) model was trained using a portion of data, retaining the rest of the data to validate and test the trained model. The model was developed for predictions ahead by 1-day and 2-days. Model score cut-off for predicting a potential breakdown was chosen to balance the capture rate vs false alarm percentage as these two represent a trade-off.