A newly published presentation by Accenture called Machine Learning in Banking, offers a useful review on the topic of machine learning and its applications in banking and financial services. According to Accenture, machine learning looks as if it will be one of the top 10 tech trends of 2017. That is why it set out to answer the question: where can machine learning be applied in banking to meet business objectives and improve performance?
Leading management consulting firm, Accenture recently reported on Machine Learning in Banking and published an insightful presentation that offers a visual introduction and overview of the topic. With its review of the subject, Accenture aimed to answer the question of how banks can use machine learning technology to better meet business objectives and improve overall performance.
In the first section of the document, an introduction to machine learning is provided. The second part of the publication outlines key areas that banks could potentially benefit from the application of machine learning in four ‘deep dive’ reviews that describe each topic in greater depth.
What is machine learning?
Machine Learning is described as an application of Artificial Intelligence that enables computers to learn without having been explicitly programmed to do so. It has been advanced as the result of established statistical theory combined with more recent developments in computational power. This combination has given rise to machine learning algorithms that offer businesses with the unique opportunity to transform the various operations and services that they provide.
Machine Learning algorithms can be categorised as being either supervised or unsupervised. Learning is achieved through analysis of large volumes of quantitative and qualitative historical data, which aims to unearth trends and patterns across hundreds of different variables at unimaginably high speeds.
- In the supervised learning case knowledge gained is applied to new data with the aim of making predictions that allow for better planning for the future.
- In the unsupervised case, knowledge obtained is used to denote the data’s ‘hidden structure’ – such as for anomaly detection purposes or customer segmentation activities.
How can machine learning be applied in banking?
Considering the developments that have been made in technology such as Natural Language Processing (NLP) and voice recognition, machine learning has the potential to be applied in both back office operations and in communicating with customers and clients. In this publication, Accenture outlines key areas in which banks could benefit from the application of Machine Learning in four separate Deep Dive reviews:
Deep Dive 1 • Fraud Detection
Deep Dive 2 • Credit Risk Management
Deep Dive 3 • Risk and Finance Reporting
Deep Dive 4 • Trading Floors
To review a PDF version of the full Accenture publication on Machine Learning in Banking, click here.