Digital Transformation
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ChatBots Era Building Goal-Oriented-ChatBot
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Digital Transformation
- 1. Zagrebačka banka d.d. Ma in CS & IT Krunoslav Ris, head of EMA Development Osijek, Octobar 2019 ChatBots Era Digital Transformation
- 2. Zagrebačka banka d.d. 2 AGENDA 1 Digital Transformations – Era Of Bots 2 Why is necessary in Banking Business 3 What is the Goal 4 Where are Improvements 5 How It is measured 6 How it is made
- 3. 3 Digital transformation – Era of Bots Lorem Ipsum (UniCredit Medium) • Lorem Ipsum (UniCredit Medium) • Lorem Ipsum (UniCredit Medium) 3 3
- 4. Gartner estimates that 85% of banks and businesses will perform customer engagement with the help of AI chatbots by 2020. Banks use them to generate revenue and help customers save money by offering smart advice and directing them to better products, driving retention and winning new business. Zagrebačka banka d.d. 4 WHY IS NECESSARY Generating Revenue Banking Customer Engagement AI ChatBots Social Networks Others
- 5. 5 WHAT IS THE GOAL • Progress of Personal Banking • Powerful Automated Customer Service • Better Customer Feedback • Personalized Marketing For specific Customers • Increasing Productivity of internal Employees • Increasing of Revenue Zagrebačka banka d.d. 5
- 7. 7 • Analysis • Credit Zagrebačka banka d.d.
- 8. Zagrebačka banka d.d. 8 REVENUE MIX Interest Income 55% Payments Fee Revenue 16% Fiduciary Activities 6% Service Charges 6% Mortage Fees 5% Trading, Brokerage, Investment Banking, Equity Investment 3% Other Fee Income 9%
- 10. Zagrebačka banka d.d. 10 Where are Improvements? 1 2 3 4 Working 00/24 Bots doesn’t need rest Working on Weekends Bots doesn’t need Weekends Working on Holidays Bots doesn’t celebrate Christmas, Hanuka, Easter, New Year. Multitasking They can serve more than one customer at the same time Improvements
- 16. Training a Goal- Oriented Chatbot for Improving Streamlining processes and Customer profiling with Deep Reinforcement Learning Zagrebačka banka d.d. 16 HOW IT IS MADE Short Technical Explanation
- 17. Zagrebačka banka d.d. 17 HOW IT IS MADE Short Technical Explanation
- 18. Zagrebačka banka d.d. 18 Dialogue System Short Technical Explanation
- 19. Zagrebačka banka d.d. 19 Dialogue System Short Technical Explanation
- 20. - Hi! - Hi there. - How old are you? - Twenty-two. And you? - Me too! Wow! Zagrebačka banka d.d. 20 EXAMPLE Short Technical Explanation
- 21. - Hi! - Hi there. - How old are you? - Twenty-two. And you? - Me too! Wow! Zagrebačka banka d.d. 21 EXAMPLE Short Technical Explanation
- 22. Zagrebačka banka d.d. 22 CONTEXT OR THOUGHT VECTOR Short Technical Explanation
- 23. Zagrebačka banka d.d. 23 HOW DOES IT WORK Short Technical Explanation
- 24. Zagrebačka banka d.d. 24 Thanks for Attention How to create an agenda and separate chapters Mentors: prof. Dr. Željko Stanković prof. Dr. Zoran Avramović prof. Dr. Branko Latinović University: Pan-european University Apeiron Krunoslav Ris Head of EMA Development krunoslav.ris@unicreditgroup.zaba.hr
Editor's Notes
- 8hours shifts Worktime Weekends Holidays Nightwork Coffe breaks Lunch Breaks Emails Phones
- Where are Improvements?
- How it is made?
- A goal-oriented (GO) chatbot is created to solve a specific problem for a user. These chatbots can help people book a flight, find a reservation, check account balance, transfer money etc. Basicaly, you can train chatbot to do any manual labor that needs human interaction in virtual world. There are two main ways to train a Goal-Oriented chatbot:
- Supervised learning with an encoder-decoder that directly maps user dialogue to responses and reinforcement learning which trains a chatbot through trial-and-error conversations with either real users or a rule-based user simulator.
- Dialogue System - The dialogue system for a Goal-Oriented chatbot using reinforcement learning is split into 3 main parts:
- The Dialogue Manager (DM) Natural Language Understanding (NLU) unit Natural Language Generator (NLG) unit. Furthermore, the Dialog Manager is divided into the Dialogue State Tracker or just State Tracker and the policy for the agent itself, which is represented by a neural network in many cases. In addition the system loop contains a user with a user goal. Generative and selective models General conversation models can be simply divided into two major types — generative and selective (or ranking) models. Also, hybrid models are possible. But the common thing is that such models conceive several sentences of dialogue context and predict the answer for this context. In the picture, you can see the illustration of such systems.
- Before going deeper, we should discuss what dialogue datasets look like. All models described below are trained on pairs (context, reply). Context is several sentences (maybe one) which preceded the reply. The sentence is just a sequence of tokens from its vocabulary. For better understanding, look at the table. There is a batch of three samples extracted from raw dialogue between two persons: Note the “<eos>” (end-of-sequence) token at the end of each sentence in the batch. This special token helps neural networks to understand sentence bounds and update its internal state wisely.
- For modeling dialogue, this paper deployed a sequence-to-sequence (seq2seq) framework which emerged in the neural machine translation field and was successfully adapted to dialogue problems. The architecture consists of two RNNs with different sets of parameters. The left one (corresponding to A-B-C tokens) is called the encoder, while the right one (corresponding to <eos>-W-X-Y-Z tokens) is called the decoder.
- Each hidden state influences the next hidden state and the final hidden state can be seen as the summary of the sequence. This state is called the context or thought vector, as it represents the intention of the sequence. From the context, the decoder generates another sequence, one symbol(word) at a time. Here, at each time step, the decoder is influenced by the context and the previously generated symbols. The most disturbing one is that the model cannot handle variable length sequences. It is disturbing because almost all the sequence-to-sequence applications, involve variable length sequences. The next one is the vocabulary size. The decoder has to run softmax over a large vocabulary of say 20,000 words, for each word in the output