RPA in Nordea Company presentation of our RPA journey

Transcription

1 RPA in Nordea Company presentation of our RPA journey Kristina Grönvall, AI strategist and Project manager Nordea

2 Unlocking the power of a digital workforce should be on everyone's agenda! Almost half of the activities people are paid almost 16 trillion USD in wages to do in the global economy have the potential to be automated by adapting currently demonstrated technology While less than 5 percent of all occupations can be automated entirely using demonstrated technologies, about 60 percent of all occupations have at least 30 percent of constituent activities that could be automated. More occupations will change then will be automated away. Source: MGI 2017 The internet, robots and digitalisation of business are like the laws of nature. Their development cannot be prevented. The more we deny new business models, the more we will fall behind. Professor Bengt Holmström; Nobel Prize Winner; 19 Aug

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4 Contents RPA DEFINITION & BENEFITS FINDING THE GOOD USE CASES HOW WE ORGANISE CREATING INTELLIGENT AUTOMATION WITH RPA

5 Contents RPA DEFINITION & BENEFITS FINDING THE GOOD USE CASES HOW WE ORGANISE CREATING INTELLIGENT AUTOMATION WITH RPA

6 What exactly is Robotic Process Automation (RPA)? Key characteristics of RPA: Robotic software can rapidly model and deploy automation which is not prioritized by IT automation or more expensive to near-shore. Individual delivery teams or process excellence resources can teach robots without extensive IT training or help. Virtual workforce is running 24/7 across systems on routine work. Virtual workforce can be remotely controlled and the resource pool can be scaled to accommodate the task at hand. Robotic Process Automation (RPA) means the use of a software to execute rules-based processes as if a real person was doing them across applications. 6

7 Nordea definition - What exactly is Robotic Process Automation (RPA)? The robots are digital employees that works in the Nordea system landscape, logging onto IT applications MIMICS HUMANS Has own UserID Goes through same clicks and actions (using a robot user ID) and performing tasks, in much the same way that a human employee would do. The robots are configured and trained by an RPA Developer and managed by an RPA Controller. PERFORMS RULE- BASED PROCESSES Pre-defined rules Human employees Digital workforce WORKS ACROSS SYSTEMS Applications Web SAP Tools Mainframe IT / system applications HANDLES ALL DIGITAL DATA SOURCES CSV files, Excel documents, , PDFs, etc. 7

8 What types of benefits can be achieved with RPA? 1 Provision of greater visibility and auditability of transactions, leading to better control over end-to-end processes 6 Consistent quality guaranteed as human error is eliminated, reducing operational risk 2 Seasonal demand can be managed by deploying virtual resources at a fraction of the cost of an FTE 5 Higher staff satisfaction by eradicating monotonous tasks, allowing individuals to focus on higher value work 3 Increased productivity with the potential to operate 24/7 4 Shorter lead time - better customer satisfaction 8

9 Contents RPA DEFINITION, BENEFITS & CURRENT STATE FINDING THE GOOD USE CASES HOW WE ORGANISE CREATING INTELLIGENT AUTOMATION WITH RPA

10 What are alternatives to RPA? Robotics Process Automation (RPA) is just one of many approaches for process improvement and it should be carefully assessed if RPA is the optimal solution to a given problem. Below are some other options which might be worth considering before choosing RPA: Automate the process by building or buying an IT system that has been or is being developed for that particular process. Do not automate a flawed process. Instead re-design it from scratch and chose the right tool and technology based on the new, ideal process. If the general level of digitization is low (e.g. has paper or manual tasks involved such as snail mail or scans), consider a digitization effort prior to automation. If the process is characterized by low volume and high complexity/criticality or volatile, RPA might not be the right tool. Instead consider moving the process to NOC. IT automation Re-design Digitization Nearshoring More stable, more expensive Takes time, long term solution A predecessor/ enabler to automation The human factor (both pros and cons) 10

11 What are alternatives to RPA? RPA has been introduced as an alternative to human labour in the Nordics or in Nordea Operations Centre (NOC). An illustrative decision tree to evaluate the options and clarify driver responsibility can be seen below: PROCESS DEVELOPMENT Process development request DECISION ON AUTOMATION AND PRIORITIZATION Automation Feasible Automation not Feasible PROCESS EXECUTION Traditional automation (API integration etc.) Robotize (RPA) Perform the process in NOC Manually perform the process locally Responsibility of IT Responsibility of Business 11

12 Number of cases (Volumes) Low High How to find processes that make up good candidates for RPA? Business Prioritization Matrix What types of processes are suitable for RPA? Large part of the process is in digital and structured format Cherry pick Quick wins Process involves multiple systems / interfaces Process does not require much human input or judgement Process is rule-based Don t touch Explore alternative business case Level of transaction volume is high (both quantity and duration of performance) Process is error prone causing quality/ commercial/ regulatory risk or delays in dependent processes High Relative complexity of process Low Process has few exceptions 12

13 What are good robotics candidates? CBB DK Credit Memo Example of a bad process Comparing the cases It is a recurring task All customers in CBB need min one yearly review It s a one time off task There is a high number of cases The process is rule based if this then that The process doesn't contain a cognitive evaluation cases yearly, minimum, more cases if there is a need for new lending on excisting customers A clear credit guideline defines how all exposures are calculated The process does not involve a gut feeling or any evaluation Potentially this could be all customers in Nordea It s the exactly the same process that all customers need to follow The process is very detailed All cases need a human evaluation Process is digital No human Recurring task Rule based High number of cases The process is purely digital and technically fit All information on exposures are stored in CMS or legacy The process involves scanning a physical report in to the systems Credit Memo Bad We are always looking for new process candidates If your thinking of a candidate, don t be discouraged by the rules for new process candidates. There is plenty of examples where one time off tasks could be solved by robotics and still be a great business case. Robots are also a leaver in potential strategic propositions, e.g. if you want shorter response time to customer or happier employees. 13

14 Zooming in on a case KYC robots in CBB and PeB Process Challenge Benefit Status KYC (Know your customer) process; Ongoing due diligence on corporate and private customers Manual work was required to gather information in the KYC process, from both internal and external systems. Apx 8,8 million customers are in scope for the ODD process. Every customer needs a review min. every third year, equaling to apx. 2,9 million cases per year We estimate that ~15-60 min is saved per case, pending on segment. In addition, we were able to collect and process larger amount of data and present it in an visual appealing manner ~ since June 2017 ~ per day + ad-hoc activities (peaks) Saved time in every case Quality increase in data collected Customer satisfaction increase by faster response time from employees Employee satisfaction from not having to manually collect information from own systems Robot are triggered by an advisor or a list (excel sheet) Robot collects information from 7 systems (internal and external) After collecting, the robot sorts the information and writes to end system (KYC tool) An advisor validates the input and finalizes the case 14

15 3 out of ~250 virtual employees I'm a senior retail banking back office agent I'm a Compliance officer I'm a bookkeeper 15

16 Where are we now in Nordea?

17 Contents RPA DEFINITION & BENEFITS FINDING THE GOOD USE CASES HOW WE ORGANISE CREATING INTELLIGENT AUTOMATION WITH RPA

18 a journey from unit pioneering to group-wide ambition and alignment - Initial pilots and trials - Based on decentral initiative in some business units - No alignment yet - BluePrism contract signed Robotics gets traction across Nordea, uncoordinated. - Alignment and Op.Model initiated - Central Robotics unit established - Central targets = acceleration of RPA - Own methodology - AI strategy - Robotics IT strategy / coop. Robotics continues to increase exponentially. Org. alignment and methodology in place. - Ambitious EOY target by Nordea - Rearrange pipeline to support growth - Focus on removing structural obstacles - Enhanced alignment and coordination H H First units pioneer Robotics in Nordea on small pilot level - Small-scale implementation - 20 processes robotized x-nordea - Scattered competence built-up - Applying various consultant methodologies Robotization progress continues. Nordea initiates central alignment. - Hub n Spoke model with closer x-cooperation - Development capacity built-up - Evolution of Methodology, Roles & Resp. - ~ 200 processes equal to ~200 FTEs Focus: industrializing Robotics and multiplying business benefit. 18

19 We have struggled 19

20 Nordea RPA structure Robotics Center of Excellence Robotics Strategy & Methodology Robotics Services Robotics IT Strategy & Operating Model Technology Strategy Vendor strategy Operating Model / Hub n Spoke Access Rights Driving structural change CyberArk implementation Access Processes Other Risk Management Robot-User & Robot-User Manager Enterprise architecture for Robotics Performance tracking RPA configuration Responsible for delivery of robots Provides the underlaying technical platform Methodologies Ways of Working Change Management Incident Management Collaboration Forums Technology & tools Platform business owner Technology roadmap RPA execution Responsible for executing RPA projects successfully E2E RPA control Responsible for smooth robot operations Link towards various IT units, architecture & AI Support and maintenance

21 The Hub = RCoE The Satellites: The Spoke: the core, responsible for scaling Robotization x-nordea with high efficiency & high quality. the receiver of services by the Hub, and also responsible for decentral Robotics delivery. the service provided by Hub to Satellites, and the obligation of the Satellites to the Hub. Interpretation of model: Decentral satellites and central Hub All work acc. to aligned processes, methodologies, strategies, and systems. Cooperation = close + frequent + formalized Satellites are defined and agreed-upon Satellites enable business insights Satellite Satellite Spoke Satellite Hub Hub provides strategy and structure which Satellites must adhere to. Hub acts as link between Satellites and other internal/external parts (e.g. IT) Implementation resources both in Satellites and Hub, based on need and ability Strategic competence build-up in selected areas coordinated by Hub Satellite Satellite Hub owns ultimate accountability for Robotics capability towards Nordea Group, enabled through close cooperation with Satellites, optimal resource utilization, business case alignment, and governed central quality assurance. ROBOTICS COE Hub n Spoke model is basically a structured Hybrid model with formalized cooperation, touch-points, and governance (= spokes ) Execution Lead Subject Matter Expert RPA Analyst RPA Controller RPA Developer Quality Assurance 21

22 Current main struggles IT anchoring Access Rights Strategic pipeline Operating model Gaining efficiency in a de-central setup Going from de-central to central with legacy robots Persistence in story telling / communication 22

23 Business or IT? Why is this a re-occurring topic in many companies? RPA Configuration is not IT Development but it has many great similarities RPA has historically been sold as a business tool you don t even need to involve IT! Business starts the RPA Journey without IT onboard takes time to recover Business generally low confidence in IT Processes, especially in large organisations IT sometimes view RPA as a hack and prefer IT automation 23

24 Robotics & IT: an important cooperation RPA Development Methodology & Processes Business IT Business IT Platforms IT run the platform IT change the platform 24

25 Top 5 things to consider in the collaboration between business and IT 1. Ensure to get IT involved in your RPA journey from day 1 2. Do not fall into the trap of defining Robotics as either IT or Business it s a robot! 3. Find an operating model where you can bring the best from IT together with the best from Business 4. Ensure a strong buy-in from CTO and COO 5. Learn along the way and be open for challenge the current Business/IT relationship 25

26 Contents RPA DEFINITION & BENEFITS FINDING THE GOOD USE CASES HOW WE ORGANISE CREATING INTELLIGENT AUTOMATION WITH RPA

27 Potential impact From Macros via RPA towards Artificial Intelligence Artificial Intelligence Robotic Process Automation (RPA) Simple scripts and Excel-macros COGNITIVE AUTOMATION RULE-BASED AUTOMATION Technological advancement Most simple level of automation A macro is a simple defined procedure of rigid and repetitive process steps within ONE application (e.g. Excel) Advanced software replicating human activity providing easy straight-through-processing developed by business users. Consolidating data from multiple sources by digital assistance. Systems provide decision intelligence and support by extensive data gathering & analysis Digital agents with direct customer contact build competence through learning and observation 27

28 What is Artificial Intelligence? Simplified definition: Machines doing tasks that would be considered intelligent if done by human SENSE COMPREHEND ACT LEARN Perceive the world by acquiring and processing images, sounds and speech. Analyze and understand the information collected by adding meaning and insights. Take action in the physical world based on comprehension and understanding. Improve performance (quality, consistency, and accuracy) based on real world experiences. Often referred to as the 4 th revolution access to data, processing power and improved techniques to analyse and make sense of the data available

29 AI implies change in mind to get to exponential business transformation Challenges Change of mind Exponential change in business New regulatory environment Changing business models / eco system Let s do the best we can with what we ve got improving the core Changing customer behavior Design where we want to be and build for exponential change New competition

30 A few Nordea use cases in brief Robotics workforce ~ cases during March Nova ~ conversations during March Customer driven 1-to-1 communication using AI to provide right info at the right time and channel for all Nordea s customers AML automation managing ~ cases on yearly basis Claims automation taking out 75% of manual tasks and decreasing waiting time from 50 days to 0 days + additional 30 cases across the bank 30

31 The existing claims process fails to deliver on customer expectations Why do I have to spend months waiting for my disability payments? Current challenge - 75 days Claims handling time for disability - Lack of transparency in process and a poor customer-experience - Numerous call to our Call Centre Why it takes time - Requires manual (and human) judgement - Relies on unstructured and comprehensive input from medical doctors and NAV (social services) - Interacts with a range of different IT systems

32 AI allows Nordea Liv to automate Claims handling end to end and to exceed customer expectations Thank you! Expected results - Waiting time reduced from months to instant handling - 50 % of all claims requests handled automatically (untouched by human hands) end-to-end - 75% of all manual work removed - A scalable and reusable solution that can also be used across Nordea Automation is expected to yield high benefits quickly, and building such capabilities is also strategically important and necessary for sustainable operations across Nordea in the future

33 Detailing of solution (I/II): Segmenting of customer claims utilizing machine learning reducing manual processing with 50% SEGMENT GREEN YELLOW RED BRIEF DESCRIPTION Insurance applier was healthy when entering a pension scheme, and did not become sick during the first two years Insurance applier was healthy when entering a pension scheme, and did become sick during the first two years or has a high benefit. However, he/she must fulfill a set of criteria (higher risk contra green segment) Insurance appliers that are not in the green or yellow segment due to some manual intervention needed. For example, there is a need for the case handler to interact with IT dept COMPLEXITY OF CASE POTENTIAL AUTOMATION DEGREE KEY BENEFITS Instant time to decision Instant time to payout No handling time Significantly reduced time to decision Significantly reduced time to payout Handling time only required in some cases Decision making support to case handlers Better planning of complex cases Automated data collection and digitalization Decision making support to case handlers 33

34 Detailing of solution (II/II): Yellow track -the new claims process includes several applications of artificial intelligence Step 1 Claim received Insured request claim online Step 2 Machine learning algorithm segment case Segment the cases and automatically forward case Step 3 RPA auto-request information Letters automatically sent to NAV and doctors for relevant cases, then scanned Step 4 OCR conversion to structured text Physical responses from NAV and doctors are converted to structured data Step 5 Automatic evaluation of claim Natural language processing trained on historic data evaluates case Step 6 Automatic fraud check Machine learning algorithm checks for fraud; if flagged, manual process Step 7 RPA auto-process (most) claims Approves or rejects the claim automatically Rule Engine & Machine Learning Optical Character Recognition Text Analytics Machine Learning RPA Easy cases Do not require additional information Somewhat complex cases Requires additional information, but can be solved automatically Complex cases Must be solved manually, but received information is digitalized Fast-tracked Goes through each step or is fast-tracked Goes through each step Fully automated processing, instant payment Automatic processing or given to case handler with decision making support Manual processing by case handler, but with decision making support

35 ARTIFICIAL INTELLIGENCE CAN REVOLUTIONISE THE PROCESS FOR HANDLING CLAIMS RPA Rule Engine & Machine Learning Optical Character Recognition Text Analytics Processing the cases based on business logic Machine Learning RPA Updating systems and informing the client once the case is concluded Segmenting incoming claims to determine their path through the system Reading PDF documents and converting them to text data Extracting meaning from free-text Human Flagging cases that have a high fraud risk Taking over when a case needs human judgment

36 CLAIMS ARE SEGMENTED INTO EITHER GREEN, YELLOW OR RED USING A RULE ENGINE AND MACHINE LEARNING RPA Rule Engine & Machine Learning Optical Character Recognition Text Analytics Processing the cases based on business logic Machine Learning RPA Updating systems and informing the client once the case is concluded Segmenting incoming claims to determine their path through the system Reading PDF documents and converting them to text data Extracting meaning from free-text Human Flagging cases that have a high fraud risk Taking over when a case needs human judgment

37 GREEN CASES ARE HANDED DIRECTLY TO A ROBOT WHO PREPARES THE CASE FOR FINALISATION RPA Rule Engine & Machine Learning Optical Character Recognition Text Analytics Processing the cases based on business logic Machine Learning RPA Updating systems and informing the client once the case is concluded Segmenting incoming claims to determine their path through the system Reading PDF documents and converting them to text data Extracting meaning from free-text Human Flagging cases that have a high fraud risk Taking over when a case needs human judgment Core systems, case handling systems, CRM systems, etc.

38 BASED ON THE CONCLUSION FROM FRAUD DETECTION, A ROBOT FINALISES THE CLAIM RPA Rule Engine & Machine Learning Optical Character Recognition Text Analytics Processing the cases based on business logic Machine Learning RPA Updating systems and informing the client once the case is concluded Segmenting incoming claims to determine their path through the system Reading PDF documents and converting them to text data Extracting meaning from free-text Human Flagging cases that have a high fraud risk Taking over when a case needs human judgment

39 YELLOW CASES REQUIRE MEDICAL DOCUMENTATION, WHICH IS DIGITALISED FROM PDF S USING OCR RPA Rule Engine & Machine Learning Optical Character Recognition Text Analytics Processing the cases based on business logic Machine Learning RPA Updating systems and informing the client once the case is concluded Segmenting incoming claims to determine their path through the system Reading PDF documents and converting them to text data Extracting meaning from free-text Human Flagging cases that have a high fraud risk Taking over when a case needs human judgment Out of work due to severe RSI in left shoulder. First journal entry was in nov 1st 2013, condition occurred six months before that. No similar condition before this. Was treated in 2014 with NSAIDS, using 50mg Voltaren 1x3 for 7 days, repeatedly.

40 REQUIRED INFORMATION IS EXTRACTED FROM THE MEDICAL DOCUMENTATION USING TEXT ANALYTICS RPA Rule Engine & Machine Learning Optical Character Recognition Text Analytics Processing the cases based on business logic Machine Learning RPA Updating systems and informing the client once the case is concluded Segmenting incoming claims to determine their path through the system Reading PDF documents and converting them to text data Extracting meaning from free-text Human Flagging cases that have a high fraud risk Taking over when a case needs human judgment Repetitive Strain Injury 01/11/ /05/ /01/2014 No Voltaren 50mg

41 BASED ON THE CONCLUSION FROM FRAUD DETECTION, A ROBOT FINALISES THE CLAIM RPA Rule Engine & Machine Learning Optical Character Recognition Text Analytics Processing the cases based on business logic Machine Learning RPA Updating systems and informing the client once the case is concluded Segmenting incoming claims to determine their path through the system Reading PDF documents and converting them to text data Extracting meaning from free-text Human Flagging cases that have a high fraud risk Taking over when a case needs human judgment

42 RED CASES ARE SENT FOR HUMAN HANDLING DUE TO COMPLEXITY OR OTHER CHARACTERISTICS RPA Rule Engine & Machine Learning Optical Character Recognition Text Analytics Processing the cases based on business logic Machine Learning RPA Updating systems and informing the client once the case is concluded Segmenting incoming claims to determine their path through the system Reading PDF documents and converting them to text data Extracting meaning from free-text Human Flagging cases that have a high fraud risk Taking over when a case needs human judgment Core systems, case handling systems, CRM systems, etc.

43 BASED ON THE CONCLUSION FROM FRAUD DETECTION, A ROBOT FINALISES THE CLAIM RPA Rule Engine & Machine Learning Optical Character Recognition Text Analytics Processing the cases based on business logic Machine Learning RPA Updating systems and informing the client once the case is concluded Segmenting incoming claims to determine their path through the system Reading PDF documents and converting them to text data Extracting meaning from free-text Human Flagging cases that have a high fraud risk Taking over when a case needs human judgment

44 BASED ON THE CONCLUSION FROM FRAUD DETECTION, A ROBOT FINALISES THE CLAIM RPA Rule Engine & Machine Learning Optical Character Recognition Text Analytics Processing the cases based on business logic Machine Learning RPA Updating systems and informing the client once the case is concluded Segmenting incoming claims to determine their path through the system Reading PDF documents and converting them to text data Extracting meaning from free-text Human Flagging cases that have a high fraud risk Taking over when a case needs human judgment

45 Reflections & learning Define your business problem what are you trying to solve with AI? - e.g. customer satisfaction is low due to long handling times in Claims Think end-to-end business processing - a well defined process and system(s) help define the scope and end-state for AI automation Allow time for training of AI robot - Training of intelligence in robot starts postimplementation Define ownership and governance model of AI - owned by the business and IT? Access? Segregation of duties?

46 Q & A Kristina Grönvall AI Strategy & Acceleration Nordea Group Data Management Office Kristina.gronvall@nordea.dk