Reﬂection - Learning Launch 

 Challenge and Selection 

 My team was tasked to discover a problem / opportunity space in current   accounts receivable   ( recovery management ) solutions we oﬀer for ﬁnancial institutions and come up with ideas to   build new products leveraging analytics and digital .   The new products / solutions would deliver   distinct value proposition compared to rest of the industry . This approach will eventually let our   organization steal the market away from pricing competition driven by traditional players . 

 Building a digital product with a data science model takes signiﬁcant capital expenditure and   sizable resources and time of the organization . The technology stack keeps evolving which   would require constant eﬀort and resources to upgrade the product . It is pivotal to get the key   requirements and functionalities that delivers the much needed and anticipated customer value 

 Speciﬁc challenges 

 1.Misalignment of our current assumptions and solutions build to address these problem   spaces will lead to 

 - Product failure resulting in loss of money , time and resources - Missed opportunity of building up an alternate organizational capability - More important is our reputation in the market with broken promises / disappointed 

 customers 

 - Also possible let down of career ’s of smart team 

 2 . Collaboration and active participation of diverse stakeholders like domain smes , operations ,   analytics / digital product owners and sales team is critical to come up with eﬀective solutions 

 3 . While we have been in this service for a decade , it is possible that there are some problem   statements which we are not aware of . The chances of conﬁrmation bias is higher 

 4 . Our current customers works with wide range of payers with diﬀerent scale and market   segments . One size may not ﬁt all , we need to understand and identify a target set of   customers / payors for which the digital solution works eﬀectively and also quantify the   magnitude of change management needed 

 To mitigate conﬁrmation bias in identiﬁed problem statements and solutions and to identify the   target customers , we chose to select Learning Launch tool from Design Thinking to test and   validate our assumptions and solutions 

 The reference from Professor Andreas Hussein on Learning Launch process 

 The purpose of a learning launch is not to prove you 're smart .   The purpose of a learning   launch is not to prove you 've got a good idea .   The purpose of a learning launch is to   get   the truth and then the truth either conﬁrms or   dis - conﬁrms your assumptions . 

 We wanted to learn the true problem spaces and conﬁrm some of our assumptions and solution   ideas 

 • Learning Launch test key customer , execution and defensibility assumptions 

 • Most learning launches involve quickly engaging with customers and testing your key 

 customer assumptions 

 Here is the pictorial representation of the process : 

 Application 

 The testable hypothesis we had is 

 Improve recovery speed from current 90 days to 60 days with an intelligent workﬂow   driven by data science and digitize payments 

 Key assumptions are 

 Value Customers : 

 • Payers who do not attend or positively respond for a phone call request in ﬁrst 60 days will 

 eventually dispute and take us to arbitration phase 

 • 20 - 30 % of payees chose to pay the recovery only after receiving   a notice to act 

 arbitration(legal action ) 

 • So , early detection of these payers and negotiation process leads to early settlement at 

 optimal cost 

 • Most payees still use check as primary mentor of payment 

 Execution 

 • So , early detection of a non responder or a direct disputes will help with fast track negotiation   • Digitization of payment by EFT option will make it easy and faster by 50 - 7 days 

 Defensibility 

 • Customers will be keen to use this product if recovery time improves to 60 - 75 days 

 • Given the availability of historic data and adoption of analytics by various industries will 

 create comfort for customers to follow data science model vs trust on human intervention 

 Prioritized Key Assumptions 

 • Building a data science model with optimal result is key and takes considerable time and 

 investment . This is   make or break factor of the solution 

 • Appetite for payees to use EFT options is another critical factor given the wide range of 

 payees in the country 

 Test 

 • My team identiﬁed three of our long standing customers . These customers deals with larger 

 range of payees . 

 • We engaged on a learning launch mode for a selected payees that represents 25 - 30 % of 

 their accounts receivable business 

 • We had three teams working with these customers . We engaged on a 4 week analysis of 

 current recovery patterns with focus on the following assumptions / variables 

 • Tracked non responders for last 3 months period vs disputers 

 • Analyzed aging of payments for late payees with follow up call count and potential legal 

 notice 

 • Identiﬁed payees with transaction value and payment mode(check / eft ) 

 Findings 

 Conﬁrming Data 

 • The data and insights indicated there is a higher appetite for payee to adopt EFT and are 

 keen to look for this solution 

 • • There is a considerable lag time in initiating arbitration process on non responders 

 Non Conﬁrming Data 

 We also realized non responders are the ﬁles where we actually do n’t have valid payee name or   contact details . This was little diﬀerent from our assumption of non responders pay us only   after disputes . 

 Launching Tool ( Develop ) 

 Basis our initial ﬁndings we prioritized 

 • Intelligent workﬂow with sorting and prioritization functionalities • Standing up infrastructure for EFT payments 

 Pivot 

 • We placed our solution of building data science model on a hold mode • Rather focused on validating the accuracy of payee information we have on record 

 Insight and Approach 

 What i learned from the experience is Collaboration among diverse set of teams is very   important to focus on customer rather than weigh on our own ideas and capabilities . In our   assignment , we were so focused to bring analytics into play , however it had less relevance to   the problem statement we had pertaining to target customers . Design thinking helps us to   focus on what customer needs and how to craft our solution that is better than other   alternatives currently exists . 

 Our Learning launch assignment is highly appreciated by our customers for taking the time and   opportunity to unravel their problem space rather than pushing our product and solutions . This   has created opportunity to collaborate with customers and not just internal teams . This is a   huge win for me and my team , helps with conﬁdence and learn rather than fear of failing 

 In our assignment we did learn and launch with three players who are our long standing   relationships . From the data patterns I have seen from our experience , there is a wider range of   payees and one size does not ﬁt all . For our next wave of learning launch I anticipate to see   greater value in engaging with diﬀerent set of players based on the region , size etc . This will   help us uncover wider range of problem spaces 


EMAIL: 
ID_NUM: 
NAME_STUDENT: 
PHONE_NUM: 
STREET_ADDRESS: 
URL_PERSONAL: 
USERNAME: 

### Task: You are a PII (Personal Identity Information) Detection. Given a essays written by students, you have to identify ONLY the following:
NAME_STUDENT - The full or partial name of a student that is not necessarily the author of the essay. This excludes instructors, authors, and other person names.
EMAIL - A student’s email address.
USERNAME - A student's username on any platform.
ID_NUM - A number or sequence of characters that could be used to identify a student, such as a student ID or a social security number.
PHONE_NUM - A phone number associated with a student.
URL_PERSONAL - A URL that might be used to identify a student.
STREET_ADDRESS - A full or partial street address that is associated with the student, such as their home address.
### Input: Reﬂection - Learning Launch 

 Challenge and Selection 

 My team was tasked to discover a problem / opportunity space in current   accounts receivable   ( recovery management ) solutions we oﬀer for ﬁnancial institutions and come up with ideas to   build new products leveraging analytics and digital .   The new products / solutions would deliver   distinct value proposition compared to rest of the industry . This approach will eventually let our   organization steal the market away from pricing competition driven by traditional players . 

 Building a digital product with a data science model takes signiﬁcant capital expenditure and   sizable resources and time of the organization . The technology stack keeps evolving which   would require constant eﬀort and resources to upgrade the product . It is pivotal to get the key   requirements and functionalities that delivers the much needed and anticipated customer value 

 Speciﬁc challenges 

 1.Misalignment of our current assumptions and solutions build to address these problem   spaces will lead to 

 - Product failure resulting in loss of money , time and resources - Missed opportunity of building up an alternate organizational capability - More important is our reputation in the market with broken promises / disappointed 

 customers 

 - Also possible let down of career ’s of smart team 

 2 . Collaboration and active participation of diverse stakeholders like domain smes , operations ,   analytics / digital product owners and sales team is critical to come up with eﬀective solutions 

 3 . While we have been in this service for a decade , it is possible that there are some problem   statements which we are not aware of . The chances of conﬁrmation bias is higher 

 4 . Our current customers works with wide range of payers with diﬀerent scale and market   segments . One size may not ﬁt all , we need to understand and identify a target set of   customers / payors for which the digital solution works eﬀectively and also quantify the   magnitude of change management needed 

 To mitigate conﬁrmation bias in identiﬁed problem statements and solutions and to identify the   target customers , we chose to select Learning Launch tool from Design Thinking to test and   validate our assumptions and solutions 

 The reference from Professor Andreas Hussein on Learning Launch process 

 The purpose of a learning launch is not to prove you 're smart .   The purpose of a learning   launch is not to prove you 've got a good idea .   The purpose of a learning launch is to   get   the truth and then the truth either conﬁrms or   dis - conﬁrms your assumptions . 

 We wanted to learn the true problem spaces and conﬁrm some of our assumptions and solution   ideas 

 • Learning Launch test key customer , execution and defensibility assumptions 

 • Most learning launches involve quickly engaging with customers and testing your key 

 customer assumptions 

 Here is the pictorial representation of the process : 

 Application 

 The testable hypothesis we had is 

 Improve recovery speed from current 90 days to 60 days with an intelligent workﬂow   driven by data science and digitize payments 

 Key assumptions are 

 Value Customers : 

 • Payers who do not attend or positively respond for a phone call request in ﬁrst 60 days will 

 eventually dispute and take us to arbitration phase 

 • 20 - 30 % of payees chose to pay the recovery only after receiving   a notice to act 

 arbitration(legal action ) 

 • So , early detection of these payers and negotiation process leads to early settlement at 

 optimal cost 

 • Most payees still use check as primary mentor of payment 

 Execution 

 • So , early detection of a non responder or a direct disputes will help with fast track negotiation   • Digitization of payment by EFT option will make it easy and faster by 50 - 7 days 

 Defensibility 

 • Customers will be keen to use this product if recovery time improves to 60 - 75 days 

 • Given the availability of historic data and adoption of analytics by various industries will 

 create comfort for customers to follow data science model vs trust on human intervention 

 Prioritized Key Assumptions 

 • Building a data science model with optimal result is key and takes considerable time and 

 investment . This is   make or break factor of the solution 

 • Appetite for payees to use EFT options is another critical factor given the wide range of 

 payees in the country 

 Test 

 • My team identiﬁed three of our long standing customers . These customers deals with larger 

 range of payees . 

 • We engaged on a learning launch mode for a selected payees that represents 25 - 30 % of 

 their accounts receivable business 

 • We had three teams working with these customers . We engaged on a 4 week analysis of 

 current recovery patterns with focus on the following assumptions / variables 

 • Tracked non responders for last 3 months period vs disputers 

 • Analyzed aging of payments for late payees with follow up call count and potential legal 

 notice 

 • Identiﬁed payees with transaction value and payment mode(check / eft ) 

 Findings 

 Conﬁrming Data 

 • The data and insights indicated there is a higher appetite for payee to adopt EFT and are 

 keen to look for this solution 

 • • There is a considerable lag time in initiating arbitration process on non responders 

 Non Conﬁrming Data 

 We also realized non responders are the ﬁles where we actually do n’t have valid payee name or   contact details . This was little diﬀerent from our assumption of non responders pay us only   after disputes . 

 Launching Tool ( Develop ) 

 Basis our initial ﬁndings we prioritized 

 • Intelligent workﬂow with sorting and prioritization functionalities • Standing up infrastructure for EFT payments 

 Pivot 

 • We placed our solution of building data science model on a hold mode • Rather focused on validating the accuracy of payee information we have on record 

 Insight and Approach 

 What i learned from the experience is Collaboration among diverse set of teams is very   important to focus on customer rather than weigh on our own ideas and capabilities . In our   assignment , we were so focused to bring analytics into play , however it had less relevance to   the problem statement we had pertaining to target customers . Design thinking helps us to   focus on what customer needs and how to craft our solution that is better than other   alternatives currently exists . 

 Our Learning launch assignment is highly appreciated by our customers for taking the time and   opportunity to unravel their problem space rather than pushing our product and solutions . This   has created opportunity to collaborate with customers and not just internal teams . This is a   huge win for me and my team , helps with conﬁdence and learn rather than fear of failing 

 In our assignment we did learn and launch with three players who are our long standing   relationships . From the data patterns I have seen from our experience , there is a wider range of   payees and one size does not ﬁt all . For our next wave of learning launch I anticipate to see   greater value in engaging with diﬀerent set of players based on the region , size etc . This will   help us uncover wider range of problem spaces
### Output: 