Digital Finance in Shared Services & GBS. Deloitte: Piyush Mistry & Oscar Hamilton LBG: Steve McKenna

Transcription

1 Digital Finance in Shared Services & GBS Deloitte: Piyush Mistry & Oscar Hamilton LBG: Steve McKenna

2 Agenda Agenda Content Digital Finance of the Future Uncover the picture of what the future of Finance functions could look like Game Changer Disruptive Technologies Articulation of the digital technologies impacting Finance Demonstrations for - Robotics Process Automation - Visualisation & Advanced Analytics - Cognitive and AI LBG Case Study Discussion of LBG s digital finance journey including core finance applications, cloud and robotics Q&A Discussion 2

3 Finance is being disrupted through digital technology % to 90% of all operational finance processes will be automated for majority of fortune 500 companies 8 Key innovations impacting finance: Robotics, Big Data, Insight Analytics, Block Chain, Cognitive Computing, mobile, IoT & Cloud Industry average cost of operating current finance functions will reduce by 25% to 30% Partnership between controllers, BU finance, IT and HR will be stronger than ever before, stimulating innovation and efficiency Internal talent would have transformed with accountants acquiring advance technology and analytics skills Finance managed service providers model will change dramatically reducing the cost gap between insourcing and outsourcing Data science and information analysis will emerge as high demand finance skills and the role of transaction processes will be eliminated. The reporting landscape will transform from reactive to proactive with focus on insight, actions, predictions, visualization, cognitive analytics on any device. Close and consolidation process will transformation into a continuous process with near real time reporting Data transparency and trust will improve multiple folds, through an evolved reporting framework that seamlessly links external reporting to internal reporting The base technology required to achieve a 2020 vision is available today and is rapidly maturing 3

4 The Finance toolset game changer disruptive technologies Seven technologies have growing relevance for Finance Process Robotics Process robotics automates transaction processing and communication across multiple technology systems. Advanced Analytics Analytics has long been part of the finance arsenal, but new techniques are helping business people tackle the crunchy questions with insightful answers. Cloud Cloud is a kind of computing that uses scalable, elastic technology to deliver services over the Internet. AI and Cognitive Computing Cognitive computing and artificial intelligence (AI) simulate human thinking. This technology includes machine learning, natural language processing, speech recognition, and computer vision. Visualisation Visualization refers to the innovative use of images and interactive technology to explore large, high density data sets. Blockchain Blockchain is a digital distributed ledger, where transactions are verified and securely stored on a network of distributed and connected nodes, without a governing central authority. In-Memory Computing Key: Core modernisation Exponential In-memory computing refers to storing data in main memory to get faster response times. And because the data is compressed, storage requirements are reduced. 4

5 Please vote. 5

6 Complexity The Digital Journey We view digital as part of an evolving journey for Shared Services & GBS Time Automation & Process Improvement Advanced Analytics and Visualisation AI and Cognitive Computing 6

7 Case Study 1: Robotic Process Automation (RPA) Reporting Automation & Process Improvement 7

8 Robotic Process Automation (RPA) Automation & Process Improvement What is RPA? RPA is the software used to capture and interpret existing applications for the purpose of automating transaction processing, data manipulation and communication across multiple technology systems. Drivers Efficiency & Quality Currently, people are completing repetitive low value tasks that are rules-based. RPA unlocks capacity to allow teams to focus more on delivering greater value and insight. Scalability & Expertise What can a robot do? A Robot is programmed to work in the same way as a human would, using the 7 robotic skills listed below. This process allows complex, cross-software tasks to be automated. 1) Gather, validate and analyse structured and unstructured information 2) Record and transport information and data How do you go about selecting a process for RPA? Potential RPA candidates are measured against the below criteria to determine RPA suitability and opportunity: Rules-based/judgement based Stable Insource & Control Highly repetitive 3) Monitor, detect or report operational performance 7 Robotic Skills 4) Calculate (a position or value) and/or decide (what to do) High volume/frequency High Average Handling Time (AHT) Flexibility 5) Orchestrate and manage activities (both robotic and people based) 7) Learn, anticipate and forecast (behaviour or outcomes) 6) Communicate with and assist users, clients and customers High error rate Structured/unstructured data Systematic/manual process Governance & Compliance 8

9 Hindsight Insight Foresight Advanced Analytics & Visualisation Advanced Analytics and Visualisation Finance Analytics is moving from being a reactive and descriptive capability towards a key driver of business foresight and prescriptive intelligence Optimising Algorithms Simulation and modelling Quantitative analysis Predictive and Prescriptive Advanced forecasting Role-based performance metrics Exceptions and alerts Slice and dice queries and drill-downs Descriptive Management reporting Enterprise data management 9

10 Case Study 2: Advanced Analytics Advanced Analytics and Visualisation Visual Exploration (Data Discovery) Use of novel visualisation methods & techniques to: 1) Identify spend drivers 2) Produce cost profiles of individual markets and create cross-market bench-marking 3) Compliment traditional hypothesis cost-driven approach Data Mapping Perform data mapping to: 1) Produce a clear line of sight across multiple data sources to track the flow of expenditure 2) Be able to identify financial gaps for reconcillation between central systems and markets Data Blending Joining of multiple data sources in order to: 1) Enable complex analysis i.e. analysis of FTE/ Travel spend by grade, using central financial systems with workforce mgmt. 2) Provide further level of insight i.e. Identifying vendors to negotiate discount rates Predictive Analytics Create what- if scenarios to: 1) Predict capex expenditure 2) Forecast future spend/savings per market/category# Machine Learning Assess unstructured data to: 1) Re-classify mis-categorised spend (By Categories, GLs or Markets) 2) Derive new classifications to create further layers and enable in depth-analysis Data Mining using pattern recognition 1) Identify FTE/Cost Center/Legal entity and supplier behaviours 2) Perform internal bench-marking to analyse non-compliant / unnecessary spend 10

11 Case Study 3: Predictive Analytics Demand forecasting Advanced Analytics and Visualisation 11

12 Quantity Case Study 3: Predictive Analytics Demand forecasting Demand Forecasting Problem Advanced Analytics and Visualisation Lost Sale 610 HL : 372,000 Unsold Stock 3328 HL : 1,576,000 Actual Client Forecast Over 1.9 million for 1 product 1 country Time 12

13 Case Study 3: Predictive Analytics Demand forecasting Advanced Analytics and Visualisation The problem Improve demand forecasting Use data from 1 country across 4 financial years The mathematics The process Step 1 - Method selection Step 2 - Split data into Training and Testing Step 3 - Algorithm training on seasonality Step 4 - Algorithm training on trend Step 5 - Algorithm training on noise identification Step 6 - Algorithm build Step 7 - Analytics performance evaluation Step 8 - Analytics impact assessment 13

14 Case Study 3: Predictive Analytics Demand forecasting Method selection Advanced Analytics and Visualisation Dimensionality reduction Classification Singular Value Decomposition K-means Hierarchical clustering Principal Component Analysis Support Vector Machines Elastic Net Neural Networks Decision Trees Decision Trees Association testing Signal processing ANOVA Chi-square testing F-test ARIMA Time-series decomposition Hidden Markov Model Fast-Fourier transformation 14

15 Hectolitre Case Study 3: Predictive Analytics Demand forecasting Algorithm training on seasonality. Advanced Analytics and Visualisation Annual Pattern Time 15

16 Hectolitre Case Study 3: Predictive Analytics Demand forecasting trend. Advanced Analytics and Visualisation Trend? Time 16

17 random seasonality trend observed Case Study 3: Predictive Analytics Demand forecasting noise identification Advanced Analytics and Visualisation Time 17

18 Hectolitre Case Study 3: Predictive Analytics Demand forecasting Performance evaluation & impact assessment Advanced Analytics and Visualisation Forecast Comparison Savings through advanced analytics forecasting Savings 1.1 million 1 product 1 country Time 18

19 Cognitive Computing & AI COGNITIVE COMPUTING IS WAY TO PRESENT AND DISCUSS AI Cognitive systems mimic but do not replicate the functioning of the human brain 4 With each data point, interaction and outcome, develop & sharpen expertise 2 With each data point, interaction and outcome, develop and sharpen expertise Apply Apply context, context, understand understand imagery, speech imagery, and other speech unstructured and other data like humans unstructured do data like humans do 2 4 Learn Understand Reason Perceive Interact Grasp underlying concepts, form hypotheses, apply rules and infer and extract Grasp ideas underlying concepts, form 3 hypothesis, apply rules and infer and extract ideas 51 5 AI and Cognitive Computing Use hearing and sight Use to hearing gather information and sight from to the gather surrounding information world from the surrounding world Talk and interact with Talk humans and interact a natural with way humans in a natural way 19

20 Case Study 4 Natural Language Generation Financial reporting Qlik / Narrative Science AI and Cognitive Computing 20

21 Case Study 5 Finance Chatbots Financial cognitive assistant AI and Cognitive Computing 21

22 LBG Case Study Steve McKenna & Oscar Hamilton

23 Q&A Deloitte: Piyush Mistry & Oscar Hamilton LBG: Steve McKenna & Alex Seymour

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