Top Three Applications Of Machine Learning In Finance
The significance of machine learning technology in the financial industry
The potential of computer programs to learn self-reliantly and improve progressively unfolds new opportunities for all industries. The significance of machine learning technology in the financial industry has become seemingly more evident in recent times.
Let’s look into applications of ML in finance; why financial companies should apply machine learning technology and the solutions they can implement with it.
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Fraud Prevention
Clients’ protection against fraudulent activities is the primary responsibility of every financial institution. For every dollar lost to fraud, financial institutions pay $2.92 in recovery and associated cost. In order to prevent fraudulent, unauthorized or erroneous activities, banks need to abandon non-operational approaches to avoid obsolescence and employ sophisticated solutions to analyze high volumes of data. Fraud the Facts 2019 report uncovered the extent of crime challenges that the finance industry is committed to unraveling. Investment in advanced security systems and tech innovations has helped protect the finance industry from $2.09 billion of unauthorized fraud.
Modern technologies especially machine learning applications entail using algorithms to analyze patterns and trends and predictive analytics to block fraudulent transactions. Other strengths of machine-learning systems are faster data processing and less manual work. Feedzai, a fintech company, claims that a fine-tuned machine learning solution can identify up to 95% of all fraud and minimize the cost of manual reconciliations, which accounts now for 25% of fraud expenditures.
Process Automation
One of the most common applications of machine learning in finance is process automation which automates manual work and improves productivity. The promise of machine learning in enterprise ensures optimized costs, improved customer experiences, and scaled up services. Automation use cases of machine learning in finance include chatbots, call-center automation, paperwork automation and gamification of the employee, training and more.
JPMorgan Chase & Co. launched a Contract Intelligence (COiN) platform that leverages Natural Language Processing (NLP) to process legal documents and extract data in a small period of time. BNY Mellon integrated process automation into their banking ecosystem. This innovation is responsible for $300,000 in annual savings and has brought about a wide range of operational improvements. (Source: Appway)
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Loans and Insurance
Loans are risk-oriented by nature and it’s rather challenging to correctly predict risk per case. Machine learning algorithms can analyze a huge set of data concerning potential debtor’s general and financial background to produce an assessment of their financial condition and creditworthiness. Besides assessing the risks for each particular case, machine learning also has a more global application; detecting trends that could influence the entire domain. For example, a machine analysis might show whether a specific demographic is gradually becoming more likely to default.
This application of machine learning in financial institutions is mostly used by large companies at present but promises to become widespread in the future, possibly providing a replacement for a significant number of human jobs.
Real-World Use Cases of Machine Learning in Banking
The adoption of ML is bringing about an ever-growing list of machine learning use cases in finance. Bank of America and Wealthfront represent several financial companies using ML for their bottom-line growth.
- Bank of America has rolled out its virtual assistant, Erica, that will provide guidance and transaction assistance to customers 24/7 by using predictive analytics.
- Wealthfront, an automated investment service, uses ML to serve its primarily-millennial customer base. Through the Weatherfront app, customers can link to financial accounts, build a financial plan, and receive financial advice.
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