{"id":13525,"date":"2024-01-25T16:55:07","date_gmt":"2024-01-25T15:55:07","guid":{"rendered":"https:\/\/www.intellias.com\/?p=13525"},"modified":"2024-06-14T14:52:12","modified_gmt":"2024-06-14T12:52:12","slug":"5-use-cases-of-machine-learning-in-fintech-and-banking","status":"publish","type":"blog","link":"https:\/\/intellias.com\/5-use-cases-of-machine-learning-in-fintech-and-banking\/","title":{"rendered":"5 Use Cases of Machine Learning in Finance and Banking"},"content":{"rendered":"

Machine learning in banking is gaining popularity in the FinTech sector, from public relations to investment decisions. But how exactly can tech companies incorporate this technology in finance to drive real results? In this article, Intellias lays out machine learning use cases in finance.
\n

\n\t\t\t
\"cover-79×81\"<\/div>\n\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t
NEW AGE IN BANKING WHITEPAPER<\/div>\n\t\t\t\t\t
How banks can switch to more profitable operating models<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t Download now <\/span>\n\t\t\t<\/div>\n\t\t<\/a><\/div><\/p>\n

The profitable alliance of machine learning and finance<\/h2>\n

Custom machine learning development<\/a> is used in various aspects of our lives today. It helps us get from point A to point B, suggests what to do with pressing issues, and is getting better at holding conversations. No wonder in the world of finance we keep hearing about new machine learning use cases in banking. Applications of artificial intelligence (AI) in FinTech are predicted to be worth up to $7,305.6 million by 2022.<\/p>\n

AI and ML are the most impactful trends in the FinTech industry<\/strong><\/p>\n

\"5<\/p>\n

Source: Mediant \u2013 Mediant FinTech Trends Report<\/a><\/em><\/p>\n

Machine learning algorithms used in finance work best for pattern identification. They detect correlations among tons of sequences and events, extracting valuable information that\u2019s camouflaged among vast data sets. Such patterns are often missed or simply can\u2019t physically be detected by humans. The ability of ML in banking to learn and predict enables FinTech providers to recognize new business opportunities and work out coherent strategies.<\/p>\n

A schematic view of ML in relation to AI and big data analytics<\/strong><\/p>\n

\"5<\/p>\n

Source: The Financial Stability Board (FSB) \u2013 Artificial intelligence and machine learning in financial services<\/a><\/em><\/p>\n

Five notable uses of machine learning in finance<\/h2>\n

FinTech companies that are exploring machine learning in banking and finance<\/a> can expect higher interest from venture funds. Venture Scanner examined funding by AI tech categories and concluded that machine learning platforms and machine learning applications not only led the sector in Q2 2018 funding but dominate the industry in all-time funding.<\/p>\n

But what makes banking and finance one of the most-targeted business segments for machine learning? It\u2019s definitely the tremendous volume of data and the nearly infinite size of this segment worldwide. There are many machine learning use cases in finance, including for banking and credit offerings, payments and remittances, asset management, personal finance, and regulatory and compliance services. One of the main benefits of machine learning in banking is volumes of data \u2014 including accurate accounting records and other numbers \u2014 that have been saved by financial companies for years can now be turned into effective business drivers. Let’s dive deeper into the ML use cases in banking.<\/p>\n

Machine learning in FinTech means more loan approvals with lower risks<\/h2>\n

Interest in peer-to-peer lending has skyrocketed both on the part of borrowers and investors. Along with P2P lenders, traditional banks are also looking for new mechanisms to improve market share without additional risk. Credit scoring is one of the most useful applications of machine learning in FinTech.<\/p>\n

Machine learning use cases in finance give lenders better insights into a borrower\u2019s ability to pay by working with far more data and more complex calculations than conventional models. Machine learning processes more layers of data, and isn\u2019t limited to FICO scores and income data. Such applications of machine learning in finance open alternative data sources to lenders.<\/p>\n

Thousands of factors, such as data from social profiles, telecommunications companies, utilities, rent payments, and even health checkup records will now count. Machine learning algorithms compare aggregated data points with those of thousands of other customers to generate an accurate risk score. If a risk score is under the threshold set by the lender, a loan will be approved automatically.<\/p>\n

Machine learning algorithms at work for loan automation<\/strong><\/p>\n

\"5<\/p>\n

Source: Tieto \u2013 How machine learning can improve accuracy in credit scoring<\/a><\/em><\/p>\n

What are the benefits of machine learning in banking credit scoring?<\/strong><\/p>\n