Ère de l intelligence artificielle et si la machine nous remplaçait? Automation in Financial Services Laurent Doucet Club Banque, Finance Assurance Mardi 20 juin 2017 En partenariat avec : 1
Contents Page A. Overview 3 B. Use cases 11 C. Detailed case studies 22 D. How to move forward? 33 This document shall be treated as confidential. It has been compiled for the exclusive, internal use by our client and is not complete without the underlying detail analyses and the oral presentation. It may not be passed on and/or may not be made available to third parties without prior written consent from. Roland Berger 2
A. Overview 3
A Overview Software-driven automation has the potential to raise efficiency to the next level across industries Major efficiency levers over time Business process automation Automation of business processes using software robots Business process outsourcing/offshoring Outsourcing of operations and responsibilities to service providers in countries with lower labor costs Business process re-engineering/management Analysis and design of business processes within an organization to reduce non-value-adding work Today artificial intelligence is where the internet was in 1996" We are entering a new phase in world history One in which fewer and fewer workers will be needed to produce the goods and services for the global population" 1995 2005 2015 From offshore to no-shore" Source: Press research; Roland Berger 4
A Overview Automation platforms offer higher flexibility than traditional scripting Emerging machine learning opens up a whole new world Software-driven automation techniques Automation technique Description Examples Data characteristics Implementation effort Source: Roland Berger 0 Tailored software 1 Robotic process automation/ 2 Artificial intelligence and scripting Automation platforms (machine learning) > Scripts or tailored (enterprise) software to support a specific process or workflow > Rigid processes and high programming/testing effort are typically required > Complex reports in SAP > Tailored workflow tools > Tools and platforms that help to automate and orchestrate repetitive processes across existing systems > Software interfaces or non-invasive approaches mimicking human behavior > Automation of IT operations/tickets > Aggregation of data from multiple systems > Recognition of security threats from deviation of normal behavior > Self-driving cars learning from observing humans Structured (rigid) Structured or patterned Unstructured and unpatterned large data sets Flexibility Low Medium High Traditional approach 1/2 Deep dives presented in this document High Low Medium Tools available, usage increasing > Advanced algorithms that can handle ambiguity Self-learning replaces need for prescriptive rules > Systems that adapt their behavior based on observing humans Emerging technology, vast potential 5
A 1 Robotic Process Automation & Automation platforms RPA and automation platforms promise to automate repetitive tasks easily without the costs and limited flexibility of tailored software RPA / Automation platforms Description and examples Robotic process automation Automation platforms Details > Software that mimics human behavior at a computer, e.g., non-invasive software to automate repetitive tasks > RPA either aims to replace human labor or assist a human worker to improve efficiency > Very easy to set up and adjust making deployment feasible even for one-off tasks, e.g., WYSIWYG 1) interfaces > Dedicated systems that aim to make automation easy > Scripting/orchestration across applications using one common platform/language > Connected to existing (legacy) systems via software interfaces and APIs > Continuous improvement approach with constant creation and adaption of scripts > Monitoring, escalation, and analysis to support operations Use cases > Moving files and folders > Scrapping data from the web > Extracting and reformatting data into reports and dashboards > Merging data from multiple places > Update meta data in cloud environments > Deployment of virtual machines > Auditing and reporting the health of IT stacks in real time > Smart City systems (e.g., smart parting systems) > Allocation of cores and RAM for simulations on supercomputers 1) What You See Is What You Get Source: WorkFusion; Roland Berger 6
A 1 Robotic Process Automation & Automation platforms RPA is expected to be one of the next major disruptions to come with sharp impact on existing way of doing business RPA in a nutshell Definition Software that simulates a 'virtual person' and interacts with existing application software through rule-based tasks in the same way humans would do Robotic process automation will be the next big disruptor.( ) Every organization will find the combination that is right for it. But getting ahead of this curve is paramount because RPA is here to stay. Tanvir Khan, Dell What it is Computer coded software that: What it is not Automation is threatening to replace swats of white-collar workers, much as mechanical robots have displaced blue-collar workers on assembly lines. Wall street Journal Anything that could give rise to smarter-than-human intelligence in the form of Artificial Intelligence, brain-computer interfaces, or neuroscience-based human intelligence enhancement wins hands down beyond contest as doing the most to change the world. Nothing else is even in the same league." Eliezer Yudkowsky, Co-Founder and Research Fellow, Machine Intelligence Research Institute Replace humans in performing repetitive rulebased tasks, use logic to model decisions in the process Interact with any application or system and can work on multiple systems Process transactions, manipulate data, triggers responses and communicate with other digital systems A software with artificial intelligence or voice recognition with reply functions Physical machine processing physical things Walking, talking, independent robots, replacing humans in all their capabilities Source: Roland Berger 7
A 2 Artificial intelligence (machine learning) AI relies on machine learning techniques for problem solving A breakthrough in deep learning enabled AI for practical applications Artificial intelligence Terminology classification Artificial intelligence > Artificial intelligence extends cognitive computing by not only suggesting solutions to problems, but also by making actual decisions based on the results of data analyses > Example: Self-driving car that analyses the environment and decides to break, accelerate, or change lanes Cognitive Computing > Cognitive computing supports people in making decisions by analyzing large amounts of (unstructured) data and suggesting solutions to problems > Cognitive computing systems only support the decision making, the actual decision is taken by humans > Example: System that analyses patient data and suggests potential treatment options including advantages and disadvantages to doctors Problem solving techniques Search and optimization Constrain satisfaction Local reasoning Control theory Probabilistic reasoning Machine learning Deep Learning Support vector machines Reinforcement learning Random forest Bayesean networks Association rule learning Genetic algorithms Decision trees Most important problem solving technique for AI Source: IBM; Roland Berger 8
A 2 Artificial intelligence (machine learning) It took close to 60 years and many so-called "winters" of stagnation for AI to reach today's state Artificial intelligence Simplified history 1956-1974 Natural language > Understanding and processing natural language, e.g., translating from English to Russian Reasoning as search > Finding a problem solution by searching for the answer Microworlds > Focus on artificially simple situation, e.g., block worlds consisting of colored blocks of various shapes and sizes AI winter Issues leading to disappointment of ambitions and stop of funding > No translation of words with contextdependent meaning possible > Complexity exceeding avail. computing power > No extrapolation outside microworlds Expert systems > Solving problems in a specific domain of knowledge by using rules derived from experts > Potential use cases were: Identification of chemical compounds from spectrometer data Diagnosis of infectious blood diseases AI winter 1980-1987 1980-2012 Issues leading to disappointment of ambitions and stop of funding > No synergies in creating expert systems for different domains Proprietary algorithms for each system required Intelligent agents > Isolation of problems and finding verifiable and useful solutions > Common language allowing interaction with economics and control theory New advanced tools > Utilization of new tools like Bayesean networks Stochastic modeling Neural networks Evolutionary algorithms Breakthrough 2012 Deep learning > Utilization of multi-level neural networks to solve complex problems like picture and speech recognition Source: Press research; Roland Berger 9
A 2 Artificial intelligence (machine learning) As a response to AI's success in recent years, most global technology companies have made it one of their key priorities Artificial intelligence Projects of global technology companies Google IBM Facebook "Our deep learning tool has now been deployed in many environments, particularly across Google in many of our production systems" "Watson is the the biggest, most important thing I ve seen in my career and is IBM s fastest growing new business in terms of revenues" "We re trying to build more than 1.5 billion AI agents One for every person who uses Facebook or any of its products" > The Google Brain project investigates deep learning since 2011 and developes TensorFlow, an open source software library for artificial intelligence > In 2014, Google acquired DeepMind Technologies, the company that later developed the AlphaGo program > IBM started to develop its cognitive computing system Watson in 2005 and added deep learning algorithms after its commercialization in 2014 > Based on Watson, IBM offers solutions for R&D projects in the pharma, publishing, and biotechnology industry, self-service applications, as well as enterprise analytics > FAIR was founded in 2013 and developed several deep learning algorithms used for photo tagging and text translation as well as extensions to Torch, an open-source library for AI development > In 2015, Facebook acquired Wit.ai that currently develops its personal assistant "M" Source: Press research; Company information; Roland Berger 10
B. Use cases 11
Simplicity B Use cases Robotic Process Automation and Augmented Intelligence apply to different perimeters Application of RPA and augmented intelligence by activity type and impacts Simple and rulebased Complex and judgment based Focus on structuring processes to enable robotization Complex activity needing humanlike decisions - Augmented Intelligence sweetspot Unstructured Structure Easily robotized activity Focus on maximizing robotization and interfacing with human interaction Structured Robotic Process Automation > Software that simulates a 'virtual person' and interacts with existing application software through rulebased tasks in the same way humans would do Artificial Intelligence > Partially automates operations and enhances complex decision making through solutions combining Natural language processing, machine learning and hypothesis generation 1 1 2 3 Quality > Reliability: 100% accuracy > Improved customer satisfaction > Better decision making thanks to increased focus of staff on more added value tasks Speed > Solutions working 24/7 > Enhanced processing speed > Increased capacity to handle volume in back office leading to less demand failure in front office Cost > Quick payback ( Typically 12-24 months) > Reduced labor costs > Limited investment needed given smart interfacing with existing IT infrastructure Source: Roland Berger 12
Automation potential B Use cases In most industries, maximum AI and RPA potential is reached on back / middle-office activities and selected support functions Overview of potential use of RPA and AI technologies by function Support functions Back-office Middle-office Front-office > Highest automation potential in Back and Middle office activities driven by: input data already digitized to a large extent industrialized processes with clear rules proliferation of IT systems and tools (CRM, sales, claims, etc.) > Several support functions involving data processing can be automated to a large extent (e.g. accounting, controlling, payroll, etc.) unlike functions involving more creativity and human interactions (marketing, communication, recruitment, ) AI RPA Human Source: Roland Berger 13
B Use cases A growing number of vendors is offering software solutions for business process automation Overview of vendors (selection) Automation technique Typical use cases 1 Robotic process automation/ 2 Artificial intelligence Automation platforms (machine learning) > Automated data transfer between different systems, e.g., between CRM and ERP systems. > Error detection for large data arrays like transaction matching and account reconciliation > Automated cybersecurity incident response > Self-learning of ability to distinguish between different types of documents, e.g., between invoices, claims and questions > Chat and voice bots with ability to process natural language and to answer automatically including clarification questions if necessary Solution providers Source: Company information; Roland Berger 14
B Use cases The number of growing use cases for RPA, automation platforms, and AI confirms the large potential of software-driven automation Overview of selected use cases 1 Robotic process automation/ 2 Artificial intelligence Automation platforms (machine learning) Banking process automation (RPA) Software service automation (RPA) Automated email processing Automated claims processing Automation of SSI process in banking > Automation of review processes for banking transactions > Automated handling of software requests > Automated categorization of emails incl. recognition of key data > Automated import of data from claims in database > 85% automation of the SSI 1) process in global banking Automated cyber attack response (RPA) IT incident handling (Automation platform) Recruiting process automation Call center automation Automated energy management > Automated reaction to cyber threats > Automated solving of standard IT incidents, esp. L0 and L1 > Automated candidate search and prequalification > Automated handling of incoming customer calls > Automated optimization of data center power consumption 1) Standard settlement instruction Source: Press research; Company information; Roland Berger 15
B 1 Robotic process automation & Automation platforms RPA can be used to automate standard banking tasks, IT services, and the handling of cyber threats Automation of banking transactions / IT services / reaction to cyber threats Problem > Each day more than 2500 high-risk bank accounts with insufficient funds have to be reviewed manually Approach > RPA is used to automate the process based on predefined rules > Software accesses the bank's core systems and does not require any system changes Problem > High workload for service desk staff due to manual procedures shifts focus away from their individual customer service tasks Approach > 95% of key user administration tasks are offered via an selfservice portal by utilizing RPA > After service request, RPA performs task by emulating a human user Problem > IT personnel can only hardly handle increasing volume and high speed of cyber attacks Approach > RPA is used for automated log-out and password reset in case of multiple simultaneous logins isolation of client from LAN in case of malware detection Advantage > 80% reduction of processing costs > Process time reduction by more than 50% > Increase of consistency Advantage > Self-service portal enables 24/7 execution of key activities > Desk staff can focus on customer service instead of manual intensive tasks Advantage > Decreased response time to cyber incidents > Reduced workload for IT personnel Source: Press research; Company information; Roland Berger 16
B 2 Artificial intelligence (machine learning) Amelia understands, learns, and adapts to natural language to handle service desk and expert advisory tasks Call center automation Cognitive agent Amelia Understanding information > Amelia understands written and spoken language including contextual information > She is able to understand the user's mood Learning > Amelia learns from live interactions > If she cannot solve a problem, she hands it over to an employee and learns by listening to him Service desk / call center support Problem > The IT service desk of a large media company needs to handle more than 65,000 calls per month which leads to high workload Approach > Amelia learned to take 64% of the incoming calls through observational learning Advantage > Reduction of staffing requirements from 76 to 32 FTEs > Reduction of the mean time to resolve an issue from 18.2 to 4.5 minutes > Reduction of the average speed of an answer 1) from 55 to 21 sec. Expert advisor for field engineers Problem > Equipment troubleshooting requires large amounts of knowledge Approach > Amelia learned from machine manuals and company policies and provides guidance to engineers Advantage > Improved equipment troubleshooting in complicated situations 1) Average time it takes for a call to be answered, includes time in waiting loop and duration of time in which the agents phone is ringing Source: Company information; Roland Berger 17
B 2 Artificial intelligence (machine learning) AI solutions have already started to be implemented in Financial Services to optimize Middle Office activities (1/2) Use cases in Financial Services Middle Office Underwriting Contracts Management Middle Office Contracts Management - Operations Claims Management Solution prov. Service Client Country Year Assistant virtuel : Réponse aux questions des chargés de clientèle dans le domaine de l'assurance 2016 Analyseur d'email clients: Détection de l'intention et prise automatique des rendez-vous commerciaux / réponses à certaines demandes (ex : transmission d'attestation d'assurance) 2016 Agent conversationnel : Réponse en direct aux questions des clients (ou transfert vers un gestionnaire si la complexité est trop élevée) Moteur de recherche multi source comprenant le langage naturel : anticipation de la volumétrie des motifs d'appels au support client, pour jour la FAQ / page d'accueil en anticipation Agent conversationnel intelligent répondant aux questions des clients RBS 2016 2015 2014 Moteur de recherche intelligent multi-source (textes, images, média sociaux, ) : analyse des données client et identification des moments de vie Moteur de recherche intelligent : Recherche de toutes les données disponibles (structurées et non structurées) pour construire une vision client synthétique, 360 en temps réel 2015 2016 Source: Analyses Roland Berger 18
B 2 Artificial intelligence (machine learning) AI solutions have already started to be implemented in Financial Services to optimize Middle Office activities (2/2) Use cases in Financial Services Middle Office Underwriting Contracts Management Middle Office Contracts Management - Operations Claims Management Solution prov. Service Client Country Year Hitachi (TBC) Moteur de recherche support aux gestionnaires (call center auto) : Recherche de réponses aux questions clients grâce à la compréhension de la voix. Gain de 15% sur les temps de communication 2015 Assistant vocal KAI répondant oralement aux demandes des clients concernant leur compte bancaire 2012 Assistant virtuel: réponse aux questions des clients sur les produits et services 2013 Moteur de recherche intelligent : Automatisation du processus de KYC (recherche, agrégation, et vérification des données clients ) Agent conversationnel : Réponse aux questions des clients (après avoir été entrainée en interne au sein du "helpdesk" du service informatique) Non communiqué 2015 2016 Assistant virtuel : Réponses aux questions des clients, intégré à l'application mobile de la banque 2016 Source: Analyses Roland Berger 19
B 2 Artificial intelligence (machine learning) AI solutions have already started to be implemented in Financial Services to optimize Operations activities Use cases in Financial Services Operations Underwriting Contracts Management Middle Office Contracts Management Operations Claims Management Solution prov. Service Client Country Year Analyseur d'emails : Identification du contenu des emails client (intention, urgence) et lancement d'actions pour certaines intentions (ex : pré remplissage des champs de virement) 2016 Analyseur d'emails : Identification du contenu des emails client, routage vers le service compétent, proposition de réponses automatiques, aide à la réponse, 2014 Agent virtuel conversationnel. Réponse aux questions des clients et réalisation d'opérations : virement, analyse de dépenses, via une interface de "chat" intégré à l'application 2016 Source: Analyses Roland Berger 20
B 2 Artificial intelligence (machine learning) AI solutions have already started to be implemented in Financial Services to optimize Claims Management activities Use cases in Financial Services Claims Management Underwriting Contracts Management Middle Office Contracts Management - Operations Claims Management Solution prov. Service Client Country Year Aide à la détection de fraude : identification de profils de fraudeurs basé sur l'analyse des données clients Confidentiel 2015+ Aide à la détection de fraude : construction en temps réel d'un score qualifiant le caractère suspect ou non des déclarations de sinistres envoyées et de qualifier les types de fraudes potentiel Confidentiel 2015+ Digitalisation du traitement des sinistres, grâce à la reconnaissance du langage écrit, l'auto remplissage de formulaires, et l'interface avec de nombreux systèmes de gestion Global 2016 Application mobile de déclaration des sinistres : Scan des pièces justificatives, détection automatique des fraudes 2016 COGITO Traitement automatisé des sinistres via la reconnaissance du langage écrit, l'extraction des informations, la comparaison des informations vs. termes de l'assurance Global 2015+ Source: Analyses Roland Berger 21
C Detailed case studies 22
C 1 RPA case study Finance function automation RPA was the preferred option in a cost reduction exercise for the finance function of a global insurance company Case study Finance RPA in Insurance > Leading insurance company with operations in +40 countries and ambitious growth and profitability targets > Finance team of ~200 FTEs with Accounting team representing 60% of finance staff. Limited use of SSC or offshoring so far > Recent merger in the group has set expectations for synergies in the Finance function Context > Overall cost reduction of 20% set for the finance function, combined with a necessity to reduce headcount > Target cost reduction of 30+% for accounting as it was identified as the area with most potential > Integrate finance organizations of recently merged companies > Detailing of activities within accounting (80+ activities for 125 FTE) > Definition of baseline volume of workload (#FTE) per activity > Evaluation of potential for process automation and offshoring for each individual activity > Consolidation and challenge of results from a general perspective to increase the level of offshoring/automation while maintaining local oversight Approach Source: Roland Berger 23
Potential for robotization C 1 RPA case study Finance function automation Criteria of savings potential & digital feasibility drive the decision for automation together with local constraints Decision tree for analysis of robotization or & outsourcing potential Should the activity be robotized or off-shored? Does it have potential it be robotized? Does it have potential to be offshored to a SSC? Source: Roland Berger project experience Feasibility of robotization? Does the activity follow a process that can be largely standardized (vs. subject to many exceptions)? Does the activity follow a rule based logic that can be programmed (vs subject to judgment and interpretation)? Can the input for the activity be digitalized, in a structured and consistent format? Possible gains from robotization? Is the process (relatively) stable over time with frequency from (intra-) daily to weekly/monthly? Is the volume of workload of this activity sufficient to justify up-front investment & license cost? Is the process subject to frequent errors? Is the process centralized? Is the workload for this activity variable, leading to low team productivity? Business & legal constraints to offshoring? Is the activity subject to regulation requiring the activity to be performed at Does local level the process? involve high headcount? Is the activity required to maintain oversight over the accounts & be able to assume legal responsibility? Is the activity in strong interaction with the client? Is an error in this activity potentially impacting business result? Are specific language skills required to carry out the activity? Is in-depth personal interaction with other functions required to carry out the activity? Is the expertise required to carry out the activity available in the remote location? "When possible, robotization should be preferred over offshoring, for reasons of cost, quality & speed" Illustration of segmentation results High Low Robotization (preferred over offshoring) General ledger accounting Accounting reconciliations Cash disbursement & bank reconciliations Fixed asset accounting Core business accounting (claims & premiums) Data input from other systems (e.g. HR, operations) Retained local Regulatory obligations Client-facing roles General oversight over finance & accounting Low Offshored to SSC Regulatory obligations Client-facing roles General oversight 60% 15% Potential for offshoring 25% High 24
C 1 RPA case study Finance function automation Assessment of robotization potential was carried out on a granular list of activities with the local finance teams in the different BUs Cornerstones of the approach Illustration of analyses carried out Highly detailed mapping of activities and sub-activities (c. 80 activities for c. 125 FTEs) Determination of FTE baseline per geography Interviews with local people to understand drivers & complexity of activities Systematic assessment with local teams of possible levers per activity: Offshoring to shared service centers Robotization & automation Required to be retained in local BU Overall coherency check & further challenge of the allocation results Source: Roland Berger project experience I. Accounting/ reporting General accounting General accounting BU 1 BU 2 BU 3 I. Accounting/ reporting General accounting Entries to the general ledger (including Automation / RobotisationFinance Shared ServicFinance Shared Servi I. Accounting/ reporting General accounting General ledger change management Retained in local BU Retained in local BU Retained in local BU BU 1 BU 2 BU 3 I. Accounting/ I. Accounting/ reporting reporting General General accounting accounting Cost General and revenue accounting allocation principles a Automation / RobotisationRetained in local BU Retained in local BU I. Accounting/ I. Accounting/ reporting reporting General General accounting accounting Quality Entries assurance, to the general accounting ledger policies (including Retained Automation in local BU / RobotisationFinance Retained in local Shared BU ServicFinance Retained in local Shared BU Servi I. Accounting/ I. Accounting/ reporting reporting General General accounting accounting Controls General over ledger reconciliations change management Retained Retained local in local BU BU Retained Retained local in local BU Retained BU Retained local in local BU BU General General General accounting accounting accounting Booking Cost General of and reserve revenue accounting setting allocation principles Retained a Automation in local BU / BU RobotisationRetained 1 Retained in local BU in local BU 2 BU Automation Retained / Robotis in BU local 3 BU General General General accounting accounting accounting Booking Quality Entries of impairments assurance, to the general of accounting ledger investments policies (including Regional Retained Automation Competence in local BU / RobotisationFinance CenFinance Retained Shared in ServicFinance local Shared BU ServicFinance Retained Shared in Servi local Shared BU Servi General General General accounting accounting accounting Booking Controls General of tax over ledger reserve reconciliations change management setting Retained Retained Retained local local in local BU BU BU Retained Retained Retained local local in local BU Retained BU Retained BU Retained local local in local BU BU BU General General General accounting accounting accounting Closing Booking Cost process of and reserve revenue for full monthly, setting allocation principles quarte Automation Retained a Automation / RobotisationRetained in local BU / RobotisationRetained Retained local in local in local BU Retained BU BU Automation Retained in local BU / Robotis in local BU General General General accounting accounting accounting Closing Booking Quality Accruals of impairments assurance, of accounting investments policies Finance Regional Retained Shared Competence in local BU Service CenFinance Retained Shared Shared in ServicFinance ServicFinance local BU Retained Shared Shared in Servi Servi local BU General General General accounting accounting accounting Closing Booking Controls Accruals of tax over reserve reconciliations setting Retained Retained Retained local local in local BU BU BU Retained Retained Retained local local in local BU Retained BU Retained BU Retained local local in local BU BU BU General General General accounting accounting accounting Closing Closing Booking reports process of reserve for consolidation for full monthly, setting quarte Finance Automation Retained Shared Service / RobotisationRetained in local BU Retained Finance Shared ServicFinance local in local BU Retained BU Automation Shared in Servi local BU / Robotis General General General accounting accounting accounting Legal Closing Booking statement Accruals of impairments of investments for audit, local reports,retained Finance Regional local Shared Competence BU Service Retained CenFinance in local Shared Shared BU ServicFinance ServicFinance Retained in local Shared Shared BU Servi Servi General General General accounting accounting accounting OtherClosing Booking Accruals of tax reserve setting Retained Retained Retained local local in local BU BU BU Retained Retained Retained local local in local BU Retained BU Retained BU Retained local local in local BU BU BU Investment General General accounting accounting accounting Investment Closing Closing accounting reports process for consolidation for full monthly, quarte Finance Finance Automation Shared Shared Service Service / RobotisationRetained Finance Finance Shared Shared in ServicFinance ServicFinance local BU Retained Shared Shared in Servi Servi local BU Reinsurance General General accounting accounting Reinsurance Legal Closing statement Accruals accounting for audit, local reports,retained Finance local Shared BU Service Retained Finance local Shared BU ServicFinance Retained in local Shared BU Servi Reinsurance General General accounting accounting Group OtherClosing Accruals reinsurance accounting Automation Retained Retained / RobotisationFinance local in local BU BU Retained Retained Shared ServicFinance local in local BU Retained BU Retained Shared Servi local in local BU BU Reinsurance Investment General accounting accounting accounting Reinsurance Investment Closing accounting accounting reports for consolidation fronting Automation Finance Finance / Shared Shared RobotisationRetained Service Service Finance Finance local Shared Shared BU ServicFinance ServicFinance Retained in local Shared Shared BU Servi Servi Premiums Reinsurance General accounting accounting Premiums Reinsurance Legal statement accounting accounting for audit, local reports,retained in local BU Retained in local BU Retained in local BU Premiums Reinsurance General accounting accounting Insurance Group Other accounts reinsurance receivables accounting Automation Automation Retained / RobotisationRetained / RobotisationFinance in local BU Retained in local Shared in BU ServicFinance local BU Retained Retained in local Shared in BU Servi local BU Premiums Reinsurance Investment accounting accounting accounting Billing Reinsurance Investment and charging accounting accounting fronting Automation Automation Finance / RobotisationRetained / Shared RobotisationRetained Service Finance in local local Shared BU Retained BU ServicFinance Retained local in local Shared BU BU Servi Claims Premiums Reinsurance accounting accounting accounting Claims Premiums Reinsurance accounting accounting accounting Automation / RobotisationRetained in local BU Retained in local BU Non-insurance Premiums Reinsurance accounting accounting Accounts Insurance Group payables accounts reinsurance receivables accounting Automation Automation / RobotisationRetained / RobotisationFinance in local Shared BU ServicFinance Retained in local Shared BU Servi Non-insurance Premiums Reinsurance accounting accounting Accounts Billing Reinsurance payables and charging accounting fronting Finance Automation Automation Shared Service / RobotisationRetained / RobotisationRetained Finance Shared in ServicFinance local in local BU Retained BU Retained Shared Servi local in local BU BU Non-insurance Claims Premiums accounting accounting accounting Accounts Claims Premiums payables accounting accounting Finance Automation Shared Service / RobotisationRetained Finance Shared in ServicFinance local BU Retained Shared in Servi local BU Non-insurance Non-insurance Premiums accounting accounting Accounts Accounts Insurance payables payables accounts receivables Automation / RobotisationRetained in local BU Retained in local BU Finance Shared Service Finance Shared ServicFinance Shared Servi Non-insurance Non-insurance Premiums accounting accounting Accounts Accounts Billing payables payables and charging Finance Finance Automation Shared Shared Service Service / RobotisationRetained Finance Finance Shared Shared in ServicFinance ServicFinance local BU Retained Shared Shared in Servi Servi local BU Non-insurance Non-insurance Claims accounting accounting accountingaccounts Accounts Claims payables payables accounting Finance Finance Automation Shared Shared Service Service / RobotisationRetained Finance Finance Shared Shared in ServicFinance ServicFinance local BU Retained Shared Shared in Servi Servi local BU Non-insurance Non-insurance Non-insurance accounting accounting accounting Accounts Accounts Accounts receivables payables payables Finance Shared Service Finance Shared ServicFinance Shared Servi Non-insurance Non-insurance Non-insurance accounting accounting accounting Accounts Accounts Accounts receivables payables payables Retained Finance Finance local Shared Shared BU Service Service Retained Finance Finance local Shared Shared BU ServicFinance ServicFinance Retained in local Shared Shared BU Servi Servi Non-insurance Non-insurance Non-insurance accounting accounting accounting Accounts Accounts Accounts receivables payables payables Retained Finance Finance local Shared Shared BU Service Service Retained Finance Finance local Shared Shared BU ServicFinance ServicFinance Retained in local Shared Shared BU Servi Servi 25
C 1 RPA case study Finance function automation Through these experiences, we acquired a broad view on robotization potential within Finance functions Robotization potential across insurance companies illustration Finance process Global Corporate insurer Global service company Global re-insurer Large global life insurer Large national life/non-life insurer (1) Large national life/non-life insurer (2) Large national life/non-life insurer (3) Transactional Accounting Insurance Accounting General Accounting System support Other Finance Accounts payable + TE Accounts Receivable Bank reconciliations/ cash applications Fixed assets accounting Re insurance accounting Premiums accounting Claims accounting Investment accounting Solvency II support General accounting Third party accounting Credit control & reporting Finance solutions (MDM) Management reporting Data analytics Legal & Compliance Support Actuarial support Procurement Large scale robotization Partial robotization No robotization Source: Project experience, Roland Berger 26
C 1 RPA case study Finance function automation Robotization was often preferred to offshoring 40% of the accounting team was directly impacted by the project Return on experience of the project Achieved results > 40% of accounting organization directly impacted by the project results in a first phase, with additional potential to be further investigated at a later stage Reduction of ~25% of total staff through robotization projects, allowing the remaining organization to focus on more value adding tasks 15% of total staff relocated to a shared service center, to realize activities not suitable for automation at a lower cost > Run-rate cost reduction of ~30% compared to overall labor cost of the function, taking into account all costs related to offshoring Key learnings > Process to be optimized as much as possible before robotization, to ensure adequate quality level and limit system investments > High activity volume & frequency are preferred scope for automation to counterbalance the required investments & workload > Complexity of processes only shows when detailed analysis is carried out Source: Roland Berger 27
C 2 AI case study Watson assessment and prioritization Watson was implemented on two pilots highlighting savings opportunities of up to 50min/day on account managers by 2020 Productivity savings estimates [2016-2020] Pilots Description of levers Watson Performance Productivity savings [min/day] Email analyser > Automatized identification of e-mail intent and level of priority, sorting and visualization based on those two criterias > Automatic login into IT applications and pre-filling of some information in the target application > Customized client answer proposal > Automatic answers on simple cases > Machine learning leveraged to continuously improve successful detection rate Detection rate 90% 70% 2016 2017 2018 2019 2020 2016 2020 Virtual assistant > Chat bot to answer simple and recurring questions on products > Connection to the document database > Display of a short list of information specifically extracted Probability estimate of successful answer Link to relevant documents > Machine learning leveraged to continuously improve successful answer rate Satisfying answers rate 30% 43% 49% 55% 36% 2016 2017 2018 2019 2020 2016 2020 Total 25 min. 50 min. Source: Roland Berger 28
C 2 AI case study Watson assessment and prioritization Watson extension to new use cases was assessed through a bottom-up analysis of account managers activities Assessment of Watson potential - Analysis of an account manager typical day Split of activities per profile [hours/day] 8,0 8,0 8,0 Illustration Bottom-up analysis and sizing of e-mail activities 0,9 0,5 0,2 2,3 0,5 1,8 1,8 1,2 1,2 1,4 1,9 0,5 0,7 3,9 3,2 0,2 0,3 0,7 0,7 Operational tasks E-mails Admin. work Meetings Meetings prep. Researches Split of different e-mail intentions Difficulty Estimated time [min] Rendez-vous - Le client souhaite obtenir un rendez-vous avec son chargé de clientèle ou passer en Caisse/en Agence Contact - Le client souhaite être contacté par le Chargé de clientèle Editer - Le client souhaite que la banque lui transmette un document Document - Le client souhaite transmettre un document à la banque Proposition - Le client souhaite bénéficier d'une offre commerciale de la banque Ecriture - Le client souhaite des informations sur une ligne d'écriture de son relevé de compte (frais, commissions, etc) Communication - Le client souhaite informer la banque d'une opération à venir Virement - Le client souhaite effectuer un virement Modifier - Le client souhaite modifier un contrat Souscription - Le client souhaite ouvrir un contrat (assurance, prêt, etc) Clôturer - Le client souhaite clôturer ou résilier un contrat Négocier - Le client souhaite négocier une tarification Moyen de paiement - Le client souhaite savoir si son moyen de paiement est disponible Personnel - Le client fait part d un changement d'information le concernant Confidential Lever blocage - Le client demande le déblocage de sa carte bancaire Tarification - Le client souhaite se faire expliquer une tarification Renégocier - Le client souhaite renégocier un crédit Situation Le client fourni des informations liées à une situation débitrice Rembourser - Le client souhaite rembourser son crédit par anticipation Débloquer Le client souhaite débloquer un crédit Créancier - Le client souhaite interdire un créancier Rejeter - Le client demande de rejeter ponctuellement un prélèvement Chèque de banque - Le client souhaite l émission d un chèque de banque Résiliation Le client demande à clôturer un virement permanent ou un versement programmé Fraude Le client informe sur une fraude le concernant Opposition Le client souhaite faire une opposition de sa carte bancaire 5% 4% 4% 4% 4% 4% 4% 3% 3% 3% 3% 2% 2% 2% 2% 2% 1% 1% 1% 1% 1% 1% 0% 7% 6% 6% 2 2 2 2 4 4 4 4 4 4 4 4 4 4 6 6 6 6 6 6 8 8 8 8 8 10 Profile A Profile B Profile C Activity where Watson could prove useful in most areas Activity where Watson could prove useful in some areas Source: Roland Berger 29
C 2 AI case study Watson assessment and prioritization We identified additional AI use cases which could lead to significant productivity savings Productivity savings estimates on potential extensions [2020] - not exhaustive Description of the levers Productivity savings > Automation of answers regarding document requests (identification of detailed intent, proposition of answer with documents) > Automation of answers to information requests on fees (identification of the intent, proposition of a standardized answer for frequent cases) > ~x min / day > ~x min / day E-mail analysis Virtual Assistant > Partial automation of contract modifications or changes in client information (including field matching and manual validation) > ~x min / day > Automation of rejected payment requests > ~x min / day > Extension of the virtual assistant to additional fields Financing Insurance > ~x min / day > Automation of meeting preparation: Client history & status synthesis, Product recommendations > ~ x min / day Commercial > Partial automation of meeting minutes: Filling of specific field based on minutes in free text > ~ x min / day assistant > Client value management support: Prioritized listing of clients to contact > ~ x min / day Processing assistant > Automation of overdraft management: Recommended decision (no action /e-mail relaunch/ blockage) and standardized e-mail answers according to client history and situation Source: Roland Berger > ~ x min / day 30
C 2 AI case study Watson assessment and prioritization Overall, the productivity improvements could reach until 16% in 2020 Gains de productivité (potentiel total) sur le réseau du client [2017-20] Gains de productivité liés à Watson : Estimation de l'impact ETP en fin d'année [2017 2020; ETP 1) ] Impact en % des chargés de clientèle 4% 10% 13% 16% 4 : Nouveaux cas d usage 3 : Extension du périmètre des cas d usage actuels 2 Périmètre client, évaluation revue 1 Périmètre et évolution d'impact client Dec. 2017 Dec. 2018 Dec. 2019 Dec. 2020 Source: Analyse Roland Berger 31
C 2 AI case study Watson assessment and prioritization Watson roll-out on account managers network shows potential for 16% of productivity savings Lessons learnt Achieved results > 16% productivity savings on total account managers network Revised potential on the pilot scope based on analysis of activities and pilot results Extension of existing use cases to additional scopes New use cases identified as part of the study > Progressive ramp-up of productivity improvements over 4 years Key learnings > In-depth analysis of activities brings additional insights on AI potential > Almost all activities can be partially or totally automatized with AI (even interactions / conversations with customers) > Machine learning gives an advantage to size and experience / AI boundaries can gradually be pushed very far > 2 types of AI solutions providers : "universal" (eg. Watson) vs. "vertical" (eg. fintechs) > Social acceptance and impacts of AI solutions to be carefully handled and anticipated Source: Roland Berger 32
D. How to move forward? 33
Processes D Approach Financial Services players can consider several routes to leverage digital optimization opportunities Approaches to digital optimization Digital levers RPA AI Back-office. Marketing On-boarding C End-to-end digital reengineering > No restriction redesign > From customers perspective > Bottom-up "Reality Check" Sustainable holistic transformation Mortgage re-financing A Digital lever maximization > Systematic across all process steps > Combination of levers Focused short term results B Digital AZBB > Comprehensive activity review > Digital and non-digital levers Short/Medium-term impact 34
Technology applicability D Approach Example Many processes have a potential of using automation techniques but need to address most important challenges first Technology applicability and impact on business challenges High P&C commercial underwriting Acturial & claims analysis P&C agency support Procurement MDM Procurement BI Personal policy admin/underwriting P&C commercial claims Marketing automation/ campaign mgmt. Marketing analytics Account set-up/servicing Risk management Business banking origination/servicing Multi-channel customer mgmt. P&C personal claims Marketing MDM Stress testing Finance MDM Payments processing Transactional procurement FP&A KYC/AML Loan portfolio mgmt. Loan underwriting/origination Collections Dodd frank compliance R2R P2P Basel2 Basel implementation Retail brokerage O2C Equipment finance Retirement services AML Mortage origination Mortage servicing Sourcing/category mgmt. Supplier risk & perf. mgmt. Target functions/ processes to automate Low Auto finance Few Impacting important business challenges Many Banking & Insurance Operations Finance Risk Procurement Marketing Source: Genpact; Roland Berger Potential automation applications which large impact on important business challenges 35
D Approach Example An integrated RPA/AI roadmap needs to be designed along with a dedicated Target Operating Model to reach full potential Illustrative RPA/AI roll-out 1 Assess full potential 2 Plan transformation 3 Execute plan Realize benefits > Understand which areas/processes offer potential for automation, evaluating Data intensity Complexity Volume Criticality Stability Systems and people involved etc. > Prioritize areas of application based on savings potential vs. need for technical and organizational transformation > Technological transformation Understand options, preconditions, and limitations of automation, including dialogue with selected vendors > Organizational transformation Define TOM 1) and assess the required changes to enable automation Set-up CoE 2) : positioning in the organisation, profiles and roles & responsibilities, monitoring and evaluation guidelines Evaluate HR implications (personnel transfers, redundancies, organizational re-design, Workers Council involvement, change mgmt., etc.) > Business plan Develop a holistic business plan and transformation roadmap > Source the technology needed (make, buy, partner) > Implement successive pilots subsequently enlarging the scope > Start implementation of organizational measures and change management > Train employees in new processes > Measure results > Evaluate automation in further areas Implement continuous improvement mindset > Lower cost > Higher quality, accuracy, reliability, and compliance > Focus of workforce on high-value tasks > Build-up of critical technology knowhow for constantly rising digital penetration 1) Target operating Model 2) Center of Excellence Source: Roland Berger 36
D Approach Beyond selected enablers, full and lasting impact of digital levers relies on several sustainability factors Implementation enablers and sustainability factors of Digital levers Implementation enablers @ Cloud computing: Simple and quick deployment of technologies, limited costs to store/use large amount of data and to host solutions, ) Data Management: Availability and completeness of data, consistency of data quality, clear governance and ownership over data Visibility on activities/processes Critical to prioritize, provides initial material for each lever, enables faster time-to-market for re-engineering Sustainability Enablers factors Robot management > Adapt the organization to integrate digital management > Center of Excellence to cover governance (IT & Ops) / technology / Expertise rollout System orchestration > Critical to reach end-to-end automation where possible > Scoping and prioritization > KPIs used to monitor the orchestration Compliance evolution > Necessary to adapt to newly automatized process > Requirement of new certifications HR adaptation: > Fully leverage the downsizing or reprioritization opportunities with impacted teams > Development of new skills Source: Roland Berger 37