How is Machine Learning transforming the Financial Industry?
If you have data on things you'd like to improve, for example; inventory, fraud, customer satisfaction, employee retention patterns, etc. You could use ML to help assist or solve for those things in the future (and more).
Few industries are better suited for machine learning than finance – given its high data volumes and historical records. Algorithms are used for trading stocks, approving loans, detecting fraud, assessing risks, and underwriting insurance. They’re even used for “robo advising” customers and aligning portfolios to user goals.
Machine learning technology is making rapid gains in the financial services industry, and a growing number of companies are now using it to enhance the customer experience. Gartner even predicts that by 2020, 85% of customer relationships with an enterprise won’t involve interacting with another human.
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So, what are some of the key areas in Machine Learning?
“ML is having a profound and transformational impact across every function in financial services, and marketing is one of the areas leading the way,” says Ulku Rowe, technical director for financial services at Google Cloud and former CTO at JPMorgan Chase. “ML is helping financial services marketers to keep up with constantly evolving consumer behavior and to ensure that they get the best value out of every marketing dollar spent.”
On the one hand, financial services marketers must be at the forefront of new technology deployments. They must deliver offerings that serve current and future customer needs. And they are under intense pressure to carry out accurate marketing campaigns and specialised promotions that help drive revenue and earnings. They are constantly tasked with beating their competitors and their own performance every fiscal quarter. Meeting such demands requires a comprehensive knowledge of the customer base and specific market segments.
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Machine Learning can help companies analyse their marketing campaigns and identify areas where they need to improve. This can help them fine tune their marketing plan, resulting in higher sales.
At the same time, the financial services industry is by necessity among the most conservative because it must strictly adhere to a wide array of complex compliance regulations. Strong security is also necessary to safeguard corporate and consumer customer data assets and mitigate security risks to the institutions. ML can bolster security and mitigate risk.
The financial services industry was one of the first to adopt Artificial Intelligence (AI) in the early 80s. The complexity of the markets led to significantly larger data sets than found in other sectors and, along with the need for improved customer experience and efficiency, meant financial services as a sector was more willing than others to adopt the emerging technology.
“This is directly correlated to the increasing sophistication of algorithmic trading. Initially people were looking for simply the fastest access to markets, then the ability to control their low-latency connections better, and into the future traders will need to look across asset classes and geographies as the world becomes smaller and events in one trading centre are rapidly felt in another.”
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Algorithmic systems often making thousands or millions of trades in a day, hence the term “high-frequency trading” (HFT), which is considered to be a subset of algorithmic trading. Most hedge funds and financial institutions do not openly disclose their AI approaches to trading (for good reason), but it is believed that machine learning and deep learning are playing an increasingly important role in calibrating trading decisions in real time.
For a long time, the FX market has been consuming machine readable news which allows for faster consumption of digitally produced news from both structured sources like central banks, as well as social media. Applying AI and ML to the trading process itself as well as the post-trade and compliance functions may still need more time to develop, but there will no doubt be some some massive improvements in the next five years.
Fraud and Risk
With its capacity to learn from large datasets and establish patterns and correlations, ML can revolutionise banking operations. It can inject new efficiencies into tasks such as risk assessment, fraud detection, anti-money laundering, trading, and customer service by providing instant insights, relevant recommendations, and informed decisions in real-time
As the financial services industry continues to leverage machine learning and predictive analytics, the volume of data these firms generate and store is ballooning. Protecting that data, other sensitive assets, and business operations will only become more challenging. Firms will have to adopt new security technologies that can mitigate their security and compliance risk.
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Artificial intelligence tools are giving banks, credit card companies, and insurance companies a new way to measure the risk profiles of their customers. Previously, these organisations had to make decisions on a limited number of data points. They had the information on their customers. But there was no good way to parse that information into meaningful knowledge. Machine learning and deep learning algorithms are better at using the data to create better risk models.
The complexity of fraudulent activity, such as payment theft and money laundering, has evolved in proportionate to advancements in technology. Deep learning (DL) dramatically reduces false positives in transactional fraud. With the availability of large volumes of customer data, such as raw transactions over time (RNN) and transaction summary vectors (RNN and CNN), firms can train AI neural networks like autoencoders and models to identify irregularities in transactional activity patterns.
Data analytics using machine learning has been transformational in helping firms overcome these challenges as it picks up on unusual user behaviour to detect suspicious activity and minimise the risk of fraud, money laundering, or a breach. Similarly, data analytics technologies can be applied to compliance activities such as database auditing processes, reducing the need for human intervention and thereby easing the burden for compliance managers.
Machine learning algorithms can significantly enhance network security, too. Data scientists train a system to spot and isolate cyber threats, as machine learning is second to none in analyzing thousands of parameters and real-time. And chances are this technology will power the most advanced cybersecurity networks in the nearest future.
Cybersecurity vignette with CB Insights analyst Will Altman. Recorded June 20, 2018 at the Future of Fintech Conference.
Managing enormous volumes of data make compliance and security two of the biggest challenges for financial organisations. It is no longer enough to protect your network perimeter from attack, as the exponential growth of data and an increase in legitimate access to that data increases the likelihood of a breach on the inside. Additionally, banks are storing large volumes of data across hybrid and multi-cloud environments that provide even more opportunity for cybercriminals to get their hands on valuable assets. In short, the same data that brings new opportunities for business growth increases the security risk for financial firms.
The coming year is set to be a challenging new age for tech with many innovations and disruptions. The benefits of ML and AI are clear, and accessibility is increasing. But significant issues will still need to be addressed before its widespread impact on businesses and consumers can be fully realised. As a new decade begins, it will be interesting to see how many of these predictions come to fruition.
It should surprise no one that tech mammoths like Google, Microsoft, Amazon, and IBM are ahead of the curve on ML. All offer their own machine learning platforms, with plug-and-play solutions for many financial services.
Sara Hooker, Google AI Research Fellow, joins us at the Future of Finance Summit 2019 to share her findings. Future of Finance Summit '19 Playlist → https://...
The severe labour shortage of skilled Machine Learning engineers will make it difficult for second tier companies to keep up. While accessibility may grow and provide a gateway for midsize organisations, those already in possession of tremendous amounts of usable data and the employees capable of leveraging it will be the ones to thrive, and ultimately have the biggest advantage in terms of successful AI and machine learning integration.
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