Machine Learning in FinTech

Project Team #09
Adam DeWitt
Andrew Hendrickson
Ashwin Rajnish
Bricks Hudson
Jacob Gottschalk
 
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Bill
Classification
with Neural
Networks
Client: Allied Payment Network
Local — Fort Wayne
Payment technology for banks and credit
unions
 
 
Goal: Supplement APN PicturePay OCR
with image classification
Customers submit bill photos via
mobile app
OCR failure is handled by support team
 
 
System
Model
Sequence Model
Convolutional Neural Network Overview
Feature Learning: Kernel Convolution
Feature Learning: Max Pooling
Fully Connected Layers
Spatial X
Spatial Y
Depth / Channel
Flatten
Roadblocks and Risks
Restricted dataset access
Limited workstation access
Model training time
 
Model accuracy improvement could be difficult
Overfitting
DevOps
About Preprocessing
Functional and Non-Functional Requirements
Technologies
GitHub
 
NodeJS
 
PyTorch
Machine Learning Library for Python
 
Boto3
Amazon S3 Devt. Kit
Project Schedule
Glossary
Artificial Neural Network - layered machine learning system inspired by an account of neuron
activation in the brain.
Confidence - output indicating a machine learning program’s amount of certainty in its
classification; may be expressed numerically or verbally.
FinTech - “financial technology”, the use of new technology to compete with traditional finance
services.
GitHub - internet hosting service for software development and version control.
Machine Learning - field of understanding and building methods which leverage data to improve
performance on some set of tasks.
Minimum Viable Product - product with minimum features to be built upon before release.
Model - file which has been trained to recognize patterns.
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This project aims to supplement Allied Payment Network's PicturePay OCR with image classification using machine learning. Customers can submit bill photos via the mobile app, and any OCR failures are handled by the support team.


Uploaded on Dec 22, 2023 | 11 Views


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  1. Machine Machine Learning in Learning in FinTech FinTech Project Team #09 Adam DeWitt Andrew Hendrickson Ashwin Rajnish Bricks Hudson Jacob Gottschalk

  2. Client: Allied Payment Network Local Fort Wayne Payment technology for banks and credit unions Bill Classification with Neural Networks Goal: Supplement APN PicturePay OCR with image classification Customers submit bill photos via mobile app OCR failure is handled by support team

  3. System Model

  4. Sequence Model

  5. Convolutional Neural Network Overview

  6. Feature Learning: Kernel Convolution

  7. Feature Learning: Max Pooling

  8. Fully Connected Layers Spatial Y Spatial X Flatten

  9. Roadblocks and Risks Restricted dataset access Limited workstation access Model training time Model accuracy improvement could be difficult Overfitting

  10. DevOps

  11. About Preprocessing Improved feature learning and computation time Edge and corner detection Transformation

  12. Functional and Non-Functional Requirements Functional Requirements Nonfunctional Requirements Image input Classification in less than 5 seconds Classify image (recognize top 100 billers) 24/7 online accessibility Output classification with confidence score Classification accuracy at 70% or better Product must fit into their current modules

  13. Technologies GitHub NodeJS PyTorch Machine Learning Library for Python Boto3 Amazon S3 Devt. Kit

  14. Project Schedule October November December January February March April Complete DevOps pipeline Establish NN model with at least 50% accuracy +5% model accuracy +5% model accuracy +5% model accuracy +5% model accuracy MVP deliverable

  15. Glossary Artificial Neural Network - layered machine learning system inspired by an account of neuron activation in the brain. Confidence - output indicating a machine learning program s amount of certainty in its classification; may be expressed numerically or verbally. FinTech - financial technology , the use of new technology to compete with traditional finance services. GitHub - internet hosting service for software development and version control. Machine Learning - field of understanding and building methods which leverage data to improve performance on some set of tasks. Minimum Viable Product - product with minimum features to be built upon before release. Model - file which has been trained to recognize patterns.

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