The value of machine learning in finance is becoming more apparent by the day. As banks and other financial institutions strive to beef up security, streamline processes, and improve financial analysis, ML is becoming the technology of choice.
Machine Learning – Not Just Another Buzzword
Unlike so many hyped technologies and overrated buzzwords, machine learning is not going away — probably ever. The ability of computer programs to learn on their own and improve over time creates new opportunities for industries across the board.
Fintech Adopts Machine Learning
While it is true that the naturally conservative financial industry was not at the front of the line for ML adoption, machine learning in fintech is now a common phrase. It offers a new level of service for financial forecasting, customer service, and data security.
In this article, we examine 15 ways ML technology is transforming the financial sector. By all accounts, this list will grow exponentially over the next few years.
Applications for Machine Learning in Finance
- Fraud prevention
- Risk management
- Investment predictions
- Customer service
- Digital assistants
- Network security
- Loan underwriting
- Algorithmic trading
- Process automation
- Document interpretation
- Content creation
- Trade settlements
- Money-laundering prevention
- Custom machine learning solutions
1. Fraud Prevention
Financial service providers have no greater responsibility than protecting their clients against fraudulent activity. But for every $1 lost to fraud, financial institutions pay $2.92 in recovery and associated cost.
Machine Learning Fraud Detection Software
To win the war against financial fraud, financial companies must abandon outdated approaches. Identifying and preventing fraudulent transactions requires sophisticated solutions that can analyze high-volume data. Machine learning offers such a solution.
By spotting patterns and using predictive analytics, machine learning algorithms can block fraudulent transactions with a degree of accuracy not even possible with stand-alone AI.
2. Risk Management
2017 and 2018 saw financial institutions adopting ML solutions for financial risk management.
Machine Learning Improves Risk Management
Traditional software applications predict creditworthiness based on static information from loan applications and financial reports. Machine learning technology can go further and also identify current market trends and even relevant news items that can affect a client’s ability to pay.
Of course, risk management also extends to preventing financial crime and financial crisis prediction. Machine learning in financial services provides solutions to these and many other risk concerns.
3. Investment Predictions
In recent years, hedge funds have increasingly moved away from traditional analysis methods. Instead, they have adopted machine learning algorithms for predicting fund trends.
Machine Learning Gives Advanced Market Insights
Using machine learning, fund managers can identify market changes earlier than is possible with traditional investment models.
The potential of machine learning technology to disrupt the investment banking industry is being taken seriously by major institutions. JPMorgan, Bank of America, and Morgan Stanley are developing automated investment advisors, powered by machine learning technology. Wise fintech companies will likewise follow suit.
4. Customer Service
Poor customer service remains one of the chief complaints among financial consumers. Whether they are speaking with a human, or using a virtual assistant, customers want accurate information and fast solutions to their problems.
Machine Learning Improves Chatbot Customer Experience
Although AI chatbots have been around for awhile, customers don’t seem impressed. For many, they just don’t seem to understand the problem.
Machine learning puts a new spin on virtual assistants by enabling them to learn, rather than simply following a prescribed set of instructions.
ML-based chatbots adapts their approach based on the behavior of each customer. The result is a chatbot that acts and feels more human for an improved customer experience.
5. Digital Assistants
No company operates without sound management. And for management to be “sound,” it must function effectively and efficiently. Machine learning technology can help executives and managers to perform their jobs with greater ease than ever before.
Machine Learning Makes Digital Assistants Almost Human
We are talking about virtual assistants, and the competition to develop a winning platform is fierce. Google, Apple, Facebook, and Microsoft each have their own version of the virtual secretary. Google’s Allo, Apple’s popular Siri, and Microsoft’s Cortana currently represent the state-of-the-art in digital helpers.
ML gives the digital assistant the ability to “learn” a manager’s needs and behavior, and to adjust accordingly. In this respect, your new assistant will seem less digital the more you use it.
Marketing is just another application of machine learning for finance that benefits corporate finance and the baking domain.
Machine Learning Bring Predictive Analytics to Marketing
The ability to make predictions based on past behaviors is fundamental to any successful marketing effort. By analyzing web activity, mobile app usage, and response to previous ad campaigns, machine learning software can predict the effectiveness of a marketing strategy.
Already, ML-powered marketing platforms are bringing advanced, predictive marketing capabilities to the marketing industry.
As financial institutions opt-in for machine learning solutions, ML tools will be forefront and center of their marketing strategies.
7. Network Security
Data security is at the top of the list whenever financial institutions are asked about their concerns. And based on the number of breaches in recent years, they have reason to worry.
Machine Learning Uniquely Suited for Protecting Financial Data
The challenge to identify modern sophisticated cyber attacks cannot be relegated to yesterday’s security software. To meet the security threats financial institutions now face requires advanced technology.
Machine learning security solutions are uniquely capable of securing the world’s financial data. The power of intelligent pattern analysis, combined with big data capabilities, gives ML security technology an edge over traditional, non-AI tools.
8. Loan Underwriting
A growing number of insurance companies have turned to machine learning to help identify risks and to help set premiums. Since machine learning makes predictions based on historical patterns and current trends, it is the perfect vehicle for insurance companies to improve profitability.
Machine Learning Reduces Underwriting Risks
The same advantages apply to the banking sector. Financial institutions that offer insurance products to their clients yield the same benefits from ML as insurance companies.
Whether an institution offers loan protection, health, mortgage, or life insurance, machine learning can help manage risks.
9. Algorithmic Trading
Algorithmic trading automates the trading process by executing trades according to predefined criteria set by the trader or fund manager. In its simplest form, an “algo” trade can automatically buy (or sell) a quantity of stock when the price-per reaches a specific level.
Machine Learning Turns Algorithmic Trading Into Intelligent Trading
Machine learning technology offers a new and diverse suite of tools to make algorithmic trading more than automatic. ML makes algo trading intelligent.
ML algorithms are designed to analyze historical market behavior, determine an optimal market strategy, to make trade predictions, and more. Without ML, even AI cannot offer that.
10. Process Automation
As financial institutions transition from spreadsheets to cloud-based data storage, a tremendous opportunity emerges.
Machine Learning Automates Back-Office and Client-Facing Processes
Even though blockchains can automate many processes through smart contracts, they have limitations. Fintech companies that want to maximize their operational efficiency will add a machine learning layer to their data processes.
ML can do more than automate back-office and client-facing processes. It can interpret documents, analyze data, and propose or execute intelligent responses. The predictive power of ML goes further and identifies issues that will need human attention before they occur.
ML even puts some icing on the cake by performing real-time audits of the institution’s processes, making regulatory compliance…well, a piece of cake.
11. Document Interpretation
The legal profession rarely feels threatened. Since attorneys create the contracts, processes, and many of the regulations that govern business, why should they?
Tradition aside, machine learning is proving that even lawyers are not beyond the disruptive reach of AI.
Machine Learning Now Processes Documents at J.P. Morgan Chase
J.P. Morgan Chase bank has invested $9.6 billion in machine learning, which has already netted a huge payoff. The first fruits of their investment is Contract Intelligence, or COiN, which uses machine learning to interpret documents.
COiN reviewed 12,000 commercial credit agreements and provided analysis in a matter of seconds. What makes that remarkable is the same task takes 360,000 attorney hours to complete. That makes J.P. Morgan Chase is so happy with machine learning they can hardly count.
12. Content Creation
Attorneys are not alone in feeling obsolescence creeping their way. Writers, artists, and other content creators are likewise in the crosshairs of ML. ML-powered content creation is yet another of AI’s many talents soon to disrupt the financial sector.
Machine Learning Writes Content for Financial Sector
Advances in Natural Language Processing (NLP) and machine learning have made usable machine-generated content a reality.
Much of the written communications of financial institutions is repetitive. That said, there is little need for a Pulitzer-Prize-winning writer to create the content. Financial summaries, company profiles, and even stock reports can easily be written by ML software. And soon, much of it will be.
13. Trade Settlements
Trade settlement is the process of exchanging payment and the purchased security following a stock trade. Despite electronic transactions being completed nearly instantaneously, the trade doesn’t always happen the way it should. A number of issues can cause the trade to not complete.
Machine Learning Resolves Trade Fails
Modern trading platforms and regulatory requirements have reduced trade failures to a small percentage. Even so, in high volume trading, failed trades can still affect the efficiency of the system. Especially burdensome is the need for most failed settlements to be resolved manually.
The use use of machine learning solutions can not only identify the cause for a failed trade, but can provide a solution — usually within a fraction of a second. Even better, by identifying exceptions to normal trading patterns, ML can predict which trades are likely to fail.
14. Money-Laundering Prevention
An estimated 2%-5% of the global GDP is laundered annually. Unfortunately, banks have not been winning the battle.
Machine Learning Blocks Money Laundering Activities
Machine learning offers a long-needed solution to the age-old problem. ML is capable of identifying patterns that are unique to money laundering. ML software results in greater detection rates, fewer false positives, and easier regulatory compliance.
Amsterdam’s Commerzbank plans to use ML to automate 80% of its compliance checklist processes by 2020. The process will begin by focusing AI technology on detecting money laundering.
15. Custom Machine Learning Solutions
As financial institutions become more receptive to machine learning solutions, the question of where to acquire ML technology becomes a looming concern.
Machine Learning Solutions Developed by Outsource Providers
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.
However, as powerful as ML technology is, there is no universal financial machine learning solution that fits every need. Many financial engineering applications require a custom ML solution to be implemented.
Fintech companies that require a customized product will find a niche market of well-qualified vendors to create solutions for them.
Machine Learning Use Cases in Banking
The adoption of ML is resulting in an expanding list of machine learning use cases in finance. Bank of America and Weatherfont represent just a couple of the financial companies using ML to grow their bottom line.
- Bank of America has rolled out its virtual assistant, Erica. The chatbot will provide guidance and transaction assistance to customers 24/7 by using predictive analytics.
- Weatherfront, an automated investment service, uses ML to serve it’s primarily-millennial customer base. Through the Weatherfront app, customers can link to financial accounts, build a financial plan, and receive financial advice.
How Ignite Can Help
As a financial service provider, finding fresh, innovative ways of serving your clients and customers is key to your longevity.
That’s where Ignite comes in.
With six R&D teams throughout Europe, Ignite has the resources and experience to develop the machine learning solutions your fintech needs to succeed in the financial services sector.
Why not contact us today for a no-cost consultation?
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