Machine learning

Fintech are increasingly using machine learning which is a kind of artificial intelligence where computers can learn without being programmed, i.e. it performs better with experience, like how an ideal human being is expected to improve with experience. It is used in varied application like predictive analysis for credit defaults, fraud detection, algorithmic trading and personalised advice and interaction

Machine learning is a kind of artificial intelligence where computers have the ability to learn without being programmed, i.e. it performs better with experience, similar to how an ideal human being is expected to improve with experience

Much of machine learning overlaps with similar fields like data mining (where patterns are discovered rather than explicitly searched for) but focuses on prediction, based on previous data

Machine learning has found various applications in fintech including:

• Predictive Analysis for Credit defaults
• Improved decision making
• Fraud Detection and Identity Management
• Extraction of information from multiple sources
• Algorithmic trading
• Personalised advice and interaction

Some solutions

Predictive Analysis for Credit defaults

Prevention of loss is a key aspect of lending. Some of the previous models in predicting loss have been based on historical financial data when it is available. Fintech companies use larger data sets from different sources and analyse these to create prediction models that inevitably improve current credit risk models.

• Early warning credit card data breach and fraud reduction solution by focusing on merchants big data
• Diagnostic tool for company credit risk assessment and predicts default

Other companies who use this technology include LendingClub, Kabbage and Lendup

Improved decision making

Machine learning allows computers to process data far faster and more efficiently to improve decision making

• Early discovery of trending news that provides a time advantage for investment decisions.
• Using artificial intelligence systems to simplify and manage cash cycle management
• Statistically guide the decision making process of a customer to the most suitable product

Other companies that use this technology are Affirm, ZestFinance and BillGuard

Fraud Detection and Identity Management

Machine learning can assist by analysing patterns in historical transaction data and build a model that detect fraudulent patterns

• Algorithmic detection of credit card fraud for online transactions
• Real time fraud prevention through machine learning models analysing big data

Other companies that use this technology are Bionym, EyeVerify and BioCatch.

Extraction of information from multiple sources

Multiple independent data sets now exist, much of it user generated, e.g. social media. Several machine learning applications extract information from these sources and package them with insights that enhance primary client financial data

• Analyses social media of clients to identify triggers relating to banking
• Extract stock related conversations from social media and rates financial bloggers
• Sentiment analysis of news sources to enhance trading algorithms

Others companies that use this technology are Dataminr and AlphaSense

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