opportunities of FinTech in the insurance industry Prof. Che Lin - PowerPoint PPT Presentation

Along with AI: challenges and opportunities of FinTech in the insurance industry Prof. Che Lin National Tsing Hua University 國立清華大學電機系 林澤教授 Joint Regional Seminar 2018/7/25

About Myself • Research interests: • Deep Learning, Data Science, FinTech, Signal Processing in Wireless Communications, Optimization Theory, Systems Biology • Education: • Ph.D. in ECE, UIUC, 2008 • Advisor: Venugopal V. Veeravalli • M.S. in Applied Mathematics, UIUC, 2008 • M.S. in ECE, UIUC, 2003 • Advisor: Weng Cho Chew • BS in EE, National Taiwan University, 1999 • Honors and Awards: • Young Scholar Innovation Award, Foundation for the Advancement of Outstanding Scholarship, 2017. • CIEE Outstanding Young Electrical Engineer Award, 2015. • Best Paper Award for 2014 GIW-ISCB-ASIA conference. • Best Poster Award, International Workshop on Mathematical Issues in Information Sciences (MIIS), 2012, Xian, China. • Master Thesis Award of the Taiwan Institute of Electrical and Electronic Engineering (Advisor; 2011, 2014) • University of Illinois: E. A. Reid Fellowship Award, Spring 2008. • University of Illinois: Vodafone Fellowship, Fall 2006 - Spring 2008.

What is AI? https://goo.gl/images/7PzHKK

Who is “Master”? - Consecutive 60 victories online - Defeat top GO players: 聶衛平、柯潔、陳耀燁 https://goo.gl/images/4CFvzo

How come? https://www.bnext.com.tw/article/42607/unknown-master-beats-top-go-players

A big shock! 中國圍棋網站最近出現名為「 Master 」的神秘棋士， 它連敗中、日、韓圍棋冠軍及多名好手， 「中國棋王」柯潔也在近日成為 Master 的手下敗將， 不料柯潔今天竟突然在個人微博發文，透露自己住院， 讓不少粉絲為之心疼，安慰他：「輸個棋而已，壓力別太大」。 -- 自由時報 (Jan. 4 th , 2017) http://news.ltn.com.tw/news/world/breakingnews/1937343

No way to defeat Master? 周俊勳認為，即使「 Master 」下法是過去認為 不好的下法與位置，「但就是拿他沒辦法」。 -- 蘋果日報 (Jan. 4 th , 2017) 「 Master 」今（ 4 日）早再度現身， 台灣圍棋高手「紅面棋王」周俊勳出馬迎戰， 周俊勳使用初手天元，之後完全仿照對手下子的「模仿棋」戰術， 但仍遭「 Master 」完美破解，在第 118 手認輸投降。 -- 自由時報 (Jan. 4 th , 2017) http://www.appledaily.com.tw/realtimenews/article/new/20170104/1027615/ http://news.ltn.com.tw/news/life/breakingnews/1936664

Behind AlphaGo http://www.storm.mg/article/99782

Can human defeat AlphaGO? http://www.techapple.com/archives/4452

Rise of the Machines https://www.youtube.com/watch?v=ebph4hbcZd4

Jobs that will be replaced by robots • Sir Christopher Pissarides (Nobel Prize in Economics in 2010) • Almost certainly disappear as jobs for humans: • telemarketers (99%) • loan officers (98%) FinTech related • cashiers (97%) • legal assistants (94%) • taxi drivers (89%) • fast food chefs (81%) https://goo.gl/images/umfQkB https://goo.gl/images/4VRMZh

What is FinTech? • Fintech ( fin ancial tech nology): a broad category that refers to the innovative use of technology in the design and delivery of financial services and products.

AI in FinTech https://goo.gl/images/YrP9zX

Ris ise of of Beh ehavior vioral al Big Data (BBD) https://goo.gl/images/ZhZEHD

Jobs that will be replaced by robots • Sir Christopher Pissarides (Nobel Prize in Economics in 2010) • Almost certainly disappear as jobs for humans: • telemarketers (99%) • loan officers (98%) • cashiers (97%) • legal assistants (94%) • taxi drivers (89%) • fast food chefs (81%) Traditional way of marketing https://goo.gl/images/3fY58B

Pr Precision ecision ma marking ing bas ased ed on on BBD https://goo.gl/images/ZXjBbe

Integrating deep learning, big data analytics, ChatBot, and customer relation management systems for customer-centric precision marketing

Deep learning in a nutshell DNN https://www.kdnuggets.com/2017/04/ai-machine-learning-black-boxes-transparency-accountability.html https://hackernoon.com/challenges-in-deep-learning-57bbf6e73bb

Why deep learning? https://goo.gl/images/Lrz6ZS

Why deep learning? Source: Deep Learning, Y. Bengio, MIT

Deep learning vs traditional learning End-to-end training https://www.kdnuggets.com/2017/04/ai-machine-learning-black-boxes-transparency-accountability.html

Classification and regression problems http://kindsonthegenius.blogspot.tw/2018/01/what-is-difference-between.html

Bank marketing dataset (dataset 1) • Define business/analytics goals and performance evaluation metric • 45,211 customers; 21 input features and 1 output variable • Demographic data and previous campaign records • Age, job, marital, education • Current/previous campaign records • Social and economical context attributes • Potential business goal • Improve marketing effectiveness by targeting the right customers • Data mining goal • Predict whether a certain customer will subscribe to a term deposit or not

DNN provides accurate predictions 50% improvement over traditional marketing

DNN better with increasing data 50% improvement over traditional marketing

Credit card defaults dataset (dataset 2) • 30,000 customers; 23 input features and 1 output variable • Demographic data and credit card behavior (6 months) • Age, income, education • History/Amount of past payment; bill statement • Potential business goal • Prevent default payments by lowering risky customers’ credit amounts • Prevent default payments by supervising risky customers • Corresponding analytics goal • Predict whether a customer will default on next payment

Recurrent neural network (RNN) Handle time-series data Source: Deep Learning, Y. Bengio, MIT

Improved RNN prediction with SVM 30% improvement over traditional default detection

Next step: deploy with ChatBot http://knowledge.wharton.upenn.edu/article/rise-chatbots-time-embrace/ https://chatbotsmagazine.com/the-complete-beginner-s-guide-to-chatbots-8280b7b906ca

Hierarchical NLG w/ Linguistic Patterns Near All Bar One is a moderately priced Italian place it is called GRU Decoder Midsummer House … … is a moderately 1. Repeat-input 2. Inner-Layer Teacher Forcing 4. Others 3. Inter-Layer Teacher Forcing DECODING LAYER4 4. Curriculum Learning All Bar One is moderately priced Italian place it is called … … All Bar One is a Midsummer House … … All Bar One is moderately DECODING LAYER3 3. ADJ + ADV Bidirectional GRU Encoder All Bar One is priced place it is called Midsummer House DECODING LAYER2 2. VERB … … Italian priceRange name Semantic 1-hot [ … 1, 0, 0, 1, 0, …] All Bar One place it Midsummer House Representation Input name[Midsummer House], food[Italian], 1. NOUN + PROPN + PRON DECODING LAYER1 ENCODER priceRange[moderate], near[All Bar One] Semantics Hierarchical Decoder

ChatBot talking to you NLG Model BLEU ROUGE-1 ROUGE-2 ROUGE-L (a) Seq2Seq 44.7 51.6 19.5 40.6 (b) + Hierarchical Decoder 41.1 60.2 31.4 46.2 (c) + Hierarchical Decoder, Repeat-Input 41.2 60.5 33.8 48.6 (d) + Hierarchical Decoder, Curriculum Learning 40.9 62.9 34.5 50.1 (e) + All 44.1 67.3 38.0 53.8 (f) (e) w/ High Inner-Layer Teacher-Forcing Prob. 36.9 58.5 31.3 45.9 (g) (e) w/ High Inter-Layer Teacher-Forcing Prob. 42.5 67.3 38.7 53.3 (h) (e) w/ High Inner- and Inter-Layer Teacher-Forcing Prob. 41.7 64.5 36.6 52.0

Jobs that will be replaced by robots • Sir Christopher Pissarides (Nobel Prize in Economics in 2010) • Almost certainly disappear as jobs for humans: • telemarketers (99%) • loan officers (98%) • cashiers (97%) Actuaries (?%) • legal assistants (94%) • taxi drivers (89%) • fast food chefs (81%) https://goo.gl/images/umfQkB https://goo.gl/images/4VRMZh

Intelligent actuary in the age of AI https://goo.gl/images/djX1qk

The growth of InsurTech https://goo.gl/images/Cq9DXN https://goo.gl/images/ac6sGy

Usage-based insurance policy https://goo.gl/images/teYA8e

A single photograph to underwrite policies https://smile.lapetussolutions.com/upload

Chatbots for insurance advice https://goo.gl/images/ha558f

AI vs Human https://goo.gl/images/s8ij8z

AI will liberate human beings https://goo.gl/images/QTnUMG

Along with AI https://goo.gl/images/beV5dv