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- 1. AI to open more doors in PFM skreddy99 skreddy99 1
- 2. Heads you win $100 or Tails you lose $105 2 Would you take this bet?
- 3. Heads you win $105 or Tails you lose $100 3 How about the reverse bet?
- 4. • For most people, it’s $200 or more • That’s Loss-Aversion in action, it doesn’t mean you won’t take risks, but to risk a loss you need to be paid a bonus or premium 4 How much would you have to win to risk losing $100 on a coin flip?
- 5. Which prospect (choice) would you make? Accept a sure loss of $75 or 75% Chance of a $100 loss 25% Chance of no loss 5 87% Prospect Theory: Loss-Aversion means Losses hurt more than same-sized Gains give Pleasure Most Surprising: Loss Aversion can even cause Investors to Seek Risk!
- 7. Credit scores using non-traditional data https://digitalcommons.law.yale.edu/cgi/viewcontent.cgi?article=1122&context=yjolt 7
- 8. https://www.fico.com/blogs/analytics-optimization/using-alternative-data-in-credit-risk-modeling/ FICO - Using Alternative Data in Credit Risk Modelling Estimated 3 billion adults worldwide who don’t have credit and so don’t have credit records 8
- 9. ZestFinance's modeling & scoring process https://digitalcommons.law.yale.edu/cgi/viewcontent.cgi?article=1122&context=yjolt 9
- 10. Predicting creditworthiness in retail banking with limited scoring data https://www.sciencedirect.com/science/article/pii/S0950705116300156?via%3Dihub 10
- 11. 11 Layers of control in a Fraud Detection System
- 12. Credit Card Fraud Detection http://isyou.info/inpra/papers/inpra-v5n4-02.pdf Characteristic of the mobile payment dataset F-measure after classification 12
- 13. https://soe.rutgers.edu/sites/default/files/imce/pdfs/gset-2018/Comparative%20Analysis%20of%20Machine%20Learning%20Algorithms%20through%20Credit%20Card%20Fraud%20Detection.pdf F-1 scores of Algorithms applied to testing datasets with uncontrolled Normal-to-Fraudulent Transaction Ratios F-1 scores of Algorithms applied to testing datasets with controlled Normal-to-Fraudulent Transaction Ratios Credit Card Fraud Detection 13
- 14. “Nowcasting” Recession https://arxiv.org/pdf/1903.03202.pdf SVM: • Linear (classes separable with a linear hyperplane) • Non-linear Features 1. Monthly log difference in nonfarm payrolls 2. Log difference in average monthly price of the S&P 500 3. Production index from Manufacturing ISM Report (info about the goods market) 4. 10-year Treasury yield minus the federal funds rate SVM Dual Parameter and NBER Recessions 14
- 15. Predict stock trend-direction using sentiment from news https://arxiv.org/pdf/1812.04199.pdf 15
- 16. 16https://arxiv.org/pdf/1804.10796.pdf Handling Uncertainty in Social Lending Credit Risk Prediction The results of combining the three classifiers through a Choquet fuzzy integral approach compared to the performance of each base classifiers alone
- 17. Player Points / Experience Points (XP) Points and XP are feedback mechanics. Can track progress, as well as be used as a way to unlock new things. Award based on achievement or desired behaviour. Physical Rewards / Prizes Physical rewards and prizes can promote lots of activity and when used well, can create engagement. Be careful of promoting quantity over quality. Leaderboards / Ladders Leaderboards come in different flavours, most commonly relative or absolute. Commonly used to show people how they compare to others and so others can see them. Not for everyone. Badges / Achievements Badges and achievements are a form of feedback. Award them to people for accomplishments. Use them wisely and in a meaningful way to make them more appreciated. Virtual Economy Create a virtual economy and allow people to spend their virtual currency on real or virtual goods. Look into the legalities of this type of system and consider the long term financial costs! Lottery / Game of Chance Lotteries and games of chance are a way to win rewards with very little effort from the user. You have to be in it, to win it though! https://www.gamified.uk/user-types/gamification-mechanics-elements/ Gamification Raising Cyber Security Awareness using Gamification Achiever Challenges Challenges help keep people interested, testing their knowledge and allowing them to apply it. Overcoming challenges will make people feel they have earned their achievement. Certificates Different from general rewards and trophies, certificates are a physical symbol of mastery and achievement. They carry meaning, status and are useful. Learning / New Skills What better way to achieve mastery than to learn something new? Give your users the opportunity to learn and expand. Quests Quests give users a fixed goal to achieve. Often made up from a series of linked challenges, multiplying the feeling of achievement. Levels / Progression Levels and goals help to map a users progression through a system. It can be as important to see where you can go next as it is to see where you have been. Boss Battles Boss battles are a chance to consolidate everything you have learned and mastered in one epic challenge. Usually signals the end of the journey – and the beginning of a new one. 17
- 18. https://www.wiwi.hu-berlin.de/de/forschung/irtg/results/discussion-papers/discussion-papers-2017-1/irtg1792dp2018-062.pdf Uplift modeling 18 Customer types as per uplift modeling
- 21. https://www.wiwi.hu-berlin.de/de/forschung/irtg/results/discussion-papers/discussion-papers-2017-1/irtg1792dp2018-062.pdf Uplift gain charts across uplift modeling strategies Uplift gain chart for response modeling Uplift modeling 21
- 22. Deep Autoencoders https://arxiv.org/pdf/1903.06580.pdf Variational Autoencoder (VAE) A standard Autoencoder 22 A variational Autoencoder
- 23. https://arxiv.org/pdf/1903.06580.pdf Learning Latent Representations of Bank Customers 23
- 24. https://arxiv.org/pdf/1903.06580.pdf Latent representation of bank customers Learning Latent Representations of Bank Customers 24
- 25. 25 Predicting bankruptcy – evaluating the performance of various methods https://towardsdatascience.com/predicting-bankruptcy-f4611afe8d2c Classifiers tried 1. Logistic Regression 2. Perceptron as a classifier 3. Deep Neural Network Classifiers (with different size and depth) 4. Fischer Linear Discriminant Analysis 5. K Nearest Neighbor Classifier (with different values of k) 6. Naive Bayes Classifier 7. Decision Tree (with different bucket size thresholds) 8. Bagged Decision Trees 9. Random Forest (with different tree sizes) 10. Gradient Boosting 11. Support Vector Machines (with different kernels) Random Forest kNN
- 26. 26https://towardsdatascience.com/predicting-bankruptcy-f4611afe8d2c Model comparison Predicting bankruptcy – evaluating the performance of various methods
- 27. 27
- 28. Digitalist AI / ML Competencies Object Detection Semantic Segmentation Facial Recognition 2D to 3D Video Conversion Object Tracking in Video Interactive Chatbots Predictive maintenance Anomaly Detection Modern ML/CV Libraries: Keras, Tensorflow, Scikit-Learn © Digitalist Group I Company Confidential I 28 Applied Computer Vision and Machine Learning: OpenCV Cloud Platforms: Azure, AWS, Google Cloud Platform (GCP) Containerization and Container Orchestration: Docker, docker- compose, Kubernetes Large Scale Relational Databases: SQL Server, OLAP Cube Data Exploration and Visualization GPU Accelerated Computing https://digitalist.global/contacts/san-francisco/