SAP Leonardo Machine Learning Enabling the intelligent Bank

Transcription

1 SAP Leonardo Machine Learning Enabling the intelligent Bank Karsten Fiolka Global Head Machine Learning for Financial Services Industry, IBU Insurance (FSI), SAP SE

2 Digitization Every company to become a software-driven company by Connected Data Integration Collaboration Mobile Big Data Business Process Innovation Intelligently connecting People, Things and Businesses Machine Learning APIs Natural Language Real-time Analytics IoT Networks Microservices CUSTOMER 4

3 The digital innovation system that enables you to innovate at scale to confidently redefine your business. ǀ CUSTOMER PUBLIC 5 5

4 SAP Leonardo Digital Innovation System Design Thinking Services Data Intelligence Analytics SAP Leonardo Technologies Blockchain Internet of Things Machine Learning Big Data SAP Cloud Platform Microservices Open APIs Flexible Runtimes Integration Data Management Multi-Cloud Infrastructure Open Source Storages SAP HANA SAP HANA Vora SAP AWS S3 SWIFT Hadoop CUSTOMER 6

5 Machine learning is the reality behind artificial intelligence What is machine learning? Computers learn from data without being explicitly programmed. Machines can see, read, listen, understand, and interact. Why now? Big Data (for example, business networks, cloud applications, the Internet of Things, and SAP S/4HANA) Massive improvements in hardware (graphics processing unit [GPU] and multicore) Deep learning algorithms CUSTOMER 8

6 What processes could be relevant for Machine Learning Innovation Is the task/process you re thinking of highly labor intense task/process that binds many resources (/causes a high number of transactions per day/month/year.)? based on a highly complex set of rules (almost impossible to describe/define all needed rules)? a problem you can describe clearly? showing a regular pattern that can be learned? backed up with high volumes of historical data that could be used for training and validating the model/algorithm If not, maybe a rule-based approach could be more appropriate to follow. CUSTOMER 9

7 SAP is making the leap to become the Intelligent Enterprise Impact Transactional Enterprise Digital Enterprise Intelligent Enterprise 2020 Yesterday Enterprise software guides processes Programmed/rule based Human knowledge work 2017 First autonomous process steps with ML Learning from single customer data sets End-to-end processes require human-in-loop with some automated support End-to-end AI drives core business & support functions Highly personalized by business & user context Humans focus on exceptions and higher value work CUSTOMER Maturity 11

8 SAP Leonardo Machine Learning enables the intelligent enterprise SAP Leonardo Machine Learning & Conversational AI Business Outcomes Increase revenue with superior sales targeting and execution 76% of the world s transaction revenue 25 industries 12 lines of business Intelligent Services Intelligent Apps In-Database ML Data Science Platform Re-imagine business processes with digital intelligence Improving quality time at work for employees The world s largest business network Increased customer satisfaction with superior service Enabling product, process & business model innovations CUSTOMER 12

9 Reimagine your value chain with Machine Learning Design Trend Analysis (Face, Age, Gender, Emotion, Apparel) Inbound Logistics Image-based Purchasing Goods & Services Classification Marketing Sales & Service Personalized Design Supplier Risk Assessment Brand Impact Conversational AI Operations Outbound Logistics Social Media Analysis Service Ticketing Predictive Maintenance Routing Optimization Customer Behavior Segmentation Customer Support Solution Recommender Quality Inspection Optimal Planning & Scheduling Supply Chain Resilience Last-mile Delivery Cash Application Accounts Payable Remittance Advices Predictive Accounting SAP Business Integrity Screening Finance CUSTOMER Learning Recommender Synchronous Translation of training content Career Path Recommender Human Resources 14

10 Experience SAP Leonardo Machine Learning Front Office SAP Brand Impact SAP Service Ticket Intelligence SAP Customer Retention SAP Cash Application SAP Leonardo Machine Learning Foundation SAP Learning Recommender Back Office SAP Remittance Advice SAP Accounts Payable SAP Resume Matching CUSTOMER 15

11 SAP Leonardo Machine Learning Foundation Enabling customers and partners to build the intelligent enterprise SAP Leonardo Machine Learning Foundation Applications Ready to use Inference Ready to use Services Bring your own Model Training Customize Model Create Training SAP Cloud Platform CUSTOMER 18

12 Core Capabilities Pre-trained Services Leverage pre-trained ML services via simple Web APIs allowing immediate usage Explore a rich set of ML services such as image classification or topic detection as building blocks for creating your own intelligent app Bring your own model* Deploy, publish and run your own TensorFlow ML Model as a web service Manage your model s status, monitor resource consumption and validate quality periodically Leverage features such as authentication and scalability Customize model* Re-train and tailor image and text classification services based on your own data Simple APIs for retraining ML models no extensive machine learning knowledge required CUSTOMER *planned for Q4/

13 Some Potential for Banking Co-Innovation Co-Innovation Use Cases Service Ticket Intelligence / Ticket to action Smart current accounts Complaints Management Conversational AI Customer Retention Insights Posting Control Automation Automated post-processing of loan payments Improve post-processing for international payments Categorization of Payments CUSTOMER 39

14 Ticket to action Automation of processing to Record provides a solution for processing s by automating the categorization, extraction and labelling of relevant information from s, and triggering suitable follow-up processes like changing payment details or executing business transactions on in-force policies with data derived from the . Challenges / Pain Points for Banks High volume of s from customers Time consuming to read through every Binds many resources Errors occur when transferring data Causes Unstructured information in s can t be processed automatically Customers are reluctant to use selfservice Proposed Solution Use ML techniques for categorization / routing Extract the relevant information (e.g. via HANA Text Extraction) Trigger workflow or processing with extracted data (Give clerk the chance to overrule if needed) Rough Solution Sketch Benefits Automation of processing Reduced response time for customers Employees can focus on complex tasks Less resources needed INTERNAL Prototype Video 40

15 Complaints Management Efficient use of machine learning to optimize customer in omni-channel communication Complaints management could be divided into two parts: Avoid complaints by actively managing high risk churning customers before complaints are actually created. This is part of customer retention initiatives Effectively process incoming complaints (fast response, hit right category at first,.) by using machine learning capabilities in order to correctly understand the incoming communication via omni channel (e.g , call center, facebook,.). The information interpreted from the single communication parts will be used to come to fast, correct and consistent responses and activities. Only extraordinary situations or customers will be treated by special complaints operators. Challenges / Pain Points High load of different types of communication from customers addressing issues Time consuming to read through every or handle single customers in phone calls Binds many resources Errors occur when human interaction is needed in retrieving and documenting information from written text and speech Causes Unstructured information in s, or verbal communication cannot be processed automatically Customers are reluctant to use selfservice Proposed Solution Use ML techniques for categorization and data extraction Trigger workflow with structured data extracted from communication data Give clerk the chance to overrule in exceptional cases, if required Rough Solution Sketch Benefits Fast, consolidated and aligned response times even in high load situation for each channel Cost reduction, since less resources are required Clerks can focus on value-adding tasks Automation of communication processing (i.e. voice, ) INTERNAL 41

16 Automated post-processing of loan payments Identify target account to support straight through processing Automated post-processing of loan payments supports the clerk in processing misdirected loan payments by predicting the relevant clearing and/or loan account. Machine learning techniques enable the correct allocation of incoming loan payments lacking required payment data or expected sequence of information in the purpose note. Challenges / Pain Points for Banks Identification of clearing / loan account due to unstructured, missing or wrong payment information High costs due to manual effort for payment information corrections/completion Increased customer inquiries or even dissatisfaction due to delays in payment allocation Causes Payment information such as account or payment purpose are not provided in a structured way and/or payment can not be assigned to a corresponding receivable. Proposed Solution Use Machine Learning techniques in order to match loan payments to target account and/or receivable(s). Automate the analysis of unstructured and structured data. E.g. identify potential payment recipient by deriving missing information from various fields of the payment. Provide completed and corrected payment data to the respective interface for automatic payment processing in the system of records. INTERNAL Rough Solution Sketch Benefits Increased straight through processing of loan payments Reduced cost and time for manual post-processing of misdirected payments Support of real-time service offering of banks to their customers 44

17 Improve post-processing for international payments Proposal for corrections (step 1) and autocorrections (step 2) of payments Scenario: Processing of international payments with incorrect or missing data. Example: BIC of recipient bank not provided in XML file. Use Case: In a first step, Backoffice clerk approves or disagrees with a proposal for correction/completion offered by the system. In a second step, decision for autocorrection/autocompletion will be taken by the system without any human interaction. Both steps will be based on machine learning using structured and unstructured information extracted from the payment. Causes Free text (allowed in ISO-format for payment orders), typos, missing information, recipient account number not mandatory Proposed Solution First analyze unstructured and structured data in the payment. For example, identify a potential payment recipient (BP or the account) by deriving the missing information from various fields of the payment. Or identify the BIC based on the name or at least parts of the name/address of the recipient bank or correspondent bank(s) Then search for matching data, for example in account and business partner master data in the banking system(s) Or for bank master data in the BIC directory Support the decision making of the clerk to a certain extend before it shall be automated completely INTERNAL Rough Solution Sketch Product & Process Configuration Payment Processing Enrichment and Validation Payment Agreements Use Case Post-processing International Payments Challenges / Pain Points Payment recipient cannot be automatically identified due to missing or wrong information on the payments Name of the recipient bank / correspondent bank not clear, no automatic match with BIC-Code Manual Interpretation of structured and unstructured information provided on the payments required High number of the same type of manual error corrections Payment Initiation Clearing & Settlement Payment Capture Transmit Other Processes Benefits Higher automation rate leads to a higher straight through processing rate of payment transactions Reduced cost and time effort for post-processing Compliance with legal or customer specific service level agreements Competitive advantages - more attractive SLAs for payment processing can be offered and more flexible pricing is possible 45

18 Categorization of Payments Efficient use of machine learning to categorize payments (without rule system!) Payment Category could be used for: Profiling of the customer and comparison with peers Detect changes in payment behavior over time of single customers and make use for cross-/upsell. Give customers an overview over his payment (spend) structure Filter /detect / identification of trigger payments (e.g. which contracts does the customer have?, does the customer have a car?, does the customer smoke, have pet s, like sports and what sports?) Challenges / Pain Points High volume of data cannot be analyzed without technical support Rule based analysis is often based on trigger words which have to be maintained manually and are risk-prone Causes Unstructured information in Payments (multiple format at customer side) has to be interpreted NO fixed rule based categories Proposed Solution Interface to train the ML (Use ML text analysis for categorization) Interface that gives categorization list (words and quality of matching) Synonyms of words, contextual analysis in the ML solution INTERNAL Rough Solution Sketch Benefits Mine additional customer information Cross-/Up-Selling Get better Customer-Profiles Detect changes of customer behavior! S4B 46

19 Conversational AI human-like interaction with Software/systems powered by Machine Learning Consumers use intelligent assistants almost anywhere in their shopping world and expect the same conversational interaction with their financial services provider/it s business software! The conversational AI provides an easy access to all sources of information, products and services relevant for the employee and/or the customer interacting with the systems of the financial services provider (via chatbot, digital assistant, etc.) Challenges / Pain Points for Financial Services Providers In day-to-day business highly complex and wide spread financial services data needs to be considered and provided to customers in an easy to consume way With the paradigm shift of Artificial Intelligence, conversational UX and Machine Learning in other industries, Financial Services Providers also need to look at the bigger picture and design an experience for the screen and beyond the screen (Upcoming) Fintechs already provide easier access to financial services products, processes and related services Causes Rough Solution Sketch Benefits Customer asks for comfortable interaction with its Financial Services provider to accomplish its day to day business. Proposed Solution Use ML techniques for (natural) language processing Combine several (all) sources of information across the system landscape relevant for the context Customer Experience MACHINE LEARNING BLOCKCHAIN IoT & Supply Chain SAP HANA Business Transactions INDUSTRY Digital Core Intelligent Insights SAP Cloud Platform APIs Workforce Engagement SECURITY Spend Management SAP Leonardo Conversational AI Landscape Humanized way for the customer to interact with his Financial Services Provider Higher customer satisfaction due to ease of consumption Enter data as naturally phrased language as text, voice, or use gesture One single access point to all business processes INTERNAL OCB 48

20 Become part of the machine learning revolution.

21 Thank you. Karsten Fiolka Global Head of Machine Learning for Financial Services Industry, IBU Insurance, SAP SE Dietmar-Hopp-Allee Walldorf GERMANY Fon: +49/6227/ Mobile:

22 Links and further information Websites API Business Hub Social Media Education Early adoption and co-innovation For more information check cloudplatform.sap.com or sap.com/ml Our services are available here: on Twitter Participate in the opensap course Enterprise Machine Learning in a Nutshell A new opensap MOOC will be available by end of October Be an early adopter and co-innovate with us for next-wave cases CUSTOMER 51

23 SAP Leonardo Machine Learning Foundation Technical enablement material Detailed documentation on SAP Help Full services description available on API Business Hub INTERNAL 52

24 SAP Leonardo Machine Learning Foundation Technical enablement material Getting started tutorials on SAP.developer Information about the services on the SAP Cloud Platform page INTERNAL 53