Written on June 6, 2017

Artificial Intelligence, Machine Learning, and Deep Learning are revolutionizing the financial technology industry.

Machine Learning and Deep Learning are a growing and diverse fields of Artificial Intelligence (AI) which studies algorithms that are capable of automatically learning from data and making predictions based on data. Machine Learning and Deep Learning are two of the most exciting technological areas of AI today. Each week there are new advancements, new technologies, new applications, and new opportunities. It’s inspiring, but also overwhelming. That’s why we created this guide to help you keep pace with all these exciting developments. Whether you’re currently employed in the fintech industry, working with Produvia or just pursuing an interest in the subject, there will always be something here to inspire you!

AI Research in FinTech

In order to take advantage of exponential power of artificial intelligence, research is the first place to look. Luckily, we have done the hard work and compiled our favourite research papers as it relates to financial industry.

Financial Forecasting

Predict daily stock prices based on historical stock prices using Support Vector Machines (SVMs) (Trafalis et al. 2000)

Financial Return Volatility

Predict volatility of financial returns using Gaussian Process (Rizvi et al. 2017)

Stock Selection

Bankruptcy

Sentiment of Financial News

Bonds

Financial Microblogs and News

Predict sentiment polarity and intensity based on tweets and financial news headlines using Word Embeddings (Saleiro et al. 2017)

Behavioural Finance

Understand investors’ behavioural biases using Pseudo-Bayesian Approach (Lam et al. 2010)

Mortgage Risk

Predict mortgage risk based on housing prices, average incomes, and zip-code-level foreclosure rates, national-level prime and subprime mortgage rates using Deep Neural Network (DNN) (Sirignano et al. 2016)

Pratical AI In FinTech

There are many companies that are already using AI, machine learning and deep learning in their products and services. Here are some of our industry favourites.

Financial Forecasting

Predict daily S&P 500 closing values based on historical S&P closing values, European and Asian/Oceanian indices using Deep Learning (Google)

Access Student Affordability and Creditworthiness

Determine the creditworthiness of new and temporary international student arrivals using Machine Learning (SelfScore)

Credit Score & Loan Analysis

Predictive analysis for credit scores and bad loans (Lending Club, Kabbage, LendUp)

Accurate Decision-Making

Process data and make decisions (such as credit-related) quicker and efficient (Affirm, ZestFinance, BillGuard)

Content/Information Extraction

Extract web content — articles, publications, documents (Dataminr, Alpha Sense)

Fraud Prevention

Detect fraudulent patterns by analyzing historical transaction data (Feedzai, Nymi, Eyeverify, Biocatch)

Building Trading Algorithm

Identify signals among stock market data (KFL Capital, Binatix)

Portfolio Management

Create chatbots, aka robo-advisors, that calibrate financial portfolio based on goals and risk tolerance of the user (Betterment, Wealthfront)

Security 2.0

Create more secure user authentication security systems using Facial Recognition, Voice Recognition, and Biometrics (Facefirst, Cognitec)

Sentiment / News Analysis

Understand the emotional meaning of text using Sentiment Analysis (Hearsay)

Customer Service

Build finance-specific chat bots to help customers ask questions (Kasisto, RBS’s Luvo)

Financial Spending

Understand how account holders are spending, investing and making their financial decisions (Venmo)

AI Ideas for FinTech

Want to explore your own fintech models? There are many artificial intelligence technologies can be applied in the financial industry. Here are some ideas for your next data science project.

Financial Forecasting

Predict stock market based on S&P500 daily resolution using Deep Neural Networks (DNN)

Customer Service

Offer product or service recommendations by weighing previous account activities against current data provided by the client and from elsewhere using Machine Learning

Marketing

Predict the effectiveness of a marketing strategy for a given customer by analyzing web activity, mobile app usage, response to previous ad campaigns using Machine Learning

Financial Reports

Generate financial reports using Natural Language Generation (NLG)

Sales / Recommendations of Financial Products

Create robo-advisor to suggest portfolio changes or a particular car or home insurance plan using Natural Language Processing (NLP) and Natural Language Understanding (NLU)

Fraud Detection

Detect financial fraud using Anomaly Detection

Customer Segmentation

Segment financial customers using K-Means Clustering or a Mixture Model

Asset Direction

Asset Affects

Predict if a sharp move in one asset affects another asset using Impulse Response or Granger Causality

Asset Divergence

Predict if an asset diverges from other related assets using One-vs-Rest Multiclass Classification

Asset Movement

Predict which assets move together using Affinity Propagation or Whitney Embedding Theorem

Asset Prices

Predict what factors are driving asset pricing using Principle Component Analysis (PCA) or Independent Component Analysis (ICA)

Asset Movement

Predict if an asset will revert after moving excessively using Principle Component Analysis (PCA) or Independent Component Analysis (ICA)

Market Regime

Understand the current market regime using Softmax Function or Hidden Markov Model (HMM)

Financial Event Occurrence

Determine the probability of an event using Decision Tree or Random Forest

Market Stress

Determine most common signs of market stress using K-Means Clustering

Noisy Data

Find signals in noisy data using Low-Pass Filters, or Support Vector Machines (SVMs)

Market Volatility

Predict volatility based on a large number of input variables using Restricted Boltzmann Machine (RBM), or Support Vector Machines (SVMs)

Article/News Sentiment

Understand the sentiment of an article or news source using Bag-of-Words Models

Article/News Topic

Understand the topic of an article or news source using Term Frequency–Inverse Document Frequency (TF–IDF)

Financial Execution Speed

Understand the optimal execution speed using Partially Observable Markov Decision Process (POMDP)

Quantitative Finance

Investment Portfolios

Optimize investment portfolios in quant finance using Reinforcement Learning (RL)

Risk Management