Consumers of digital services expect quality service, security, and convenience. Machine learning (ML) in finance is the best example of how these needs can be fulfilled. Without compromising business models, and the need to generate revenue. With customer-centricity in mind. The best way to serve people what they need is to know them a little better. How is this goal achieved and what’s the state of machine learning adoption in the industry?
Read our blog post to learn more about the best use cases of machine learning in finance and the major challenges in the adoption of this technology such as:
- Shortage of in-house domain knowledge
- Low quality of gathered data
- Poor condition and structure of the software, obstructing data mining
- Low accuracy of algorithms and models
- Budget limitations
Go to our blog to discover what is the state of machine learning adoption in banking and finance.
