Detailed data analysis enables B2B companies to explore new business opportunities. Since companies are often bombarded with large volumes of leads and potential customers, a solution that can separate the signal from the noise would help discover, research, and follow-up.
FREMONT, CA: Augmented analytics and artificial intelligence are the driving force of technological revolutions. Financial services companies need detailed and accurate information to make informed decisions and provide valuable products to their customers. High-quality data allows financial institutions to make financial decisions quickly, soundly, and with less risk. Investment in AI is rapidly growing as financial services organizations continue to realize its potential.
Let us look at the impact of augmented analytics and artificial intelligence on the finance industry:
[vendor_logo_first]Credit Scoring
Deciding whether to give credit to a business or individual is a critical financial service, which requires high-quality data to make informed decisions. Offering solutions to customers with high-risk, deciding about default risk using external data, or making judgments using incomplete information can put financial services companies and their users at a high risk. AI makes it possible to paint a complete picture of creditworthiness, allowing financial services companies to make better decisions.
Investment Analysis
Detailed data analysis enables B2B companies to explore new business opportunities. Since companies are often bombarded with large volumes of leads and potential customers, a solution that can separate the signal from the noise would help discover, research, and follow-up. With the help of the machine learning (ML) model, the sourcing process can be automated, holding up only the highest potential opportunities.
Algorithmic Trading
The increased adoption of algorithmic trading allows financial service companies to keep up with demands for portfolio risk calculations and rapid pricing. These demands require high-performance computing to execute calculations quickly, which can be accomplished through deep learning. Currently, some models allow GPU-assisted processing for improved latency. Augmenting apps that are written in C++ or Python without interference, calculations can be run in minutes, which improves speed by up to 40x by placing GPUs in production. Faster processing for artificial intelligence and machine learning enables traders to make decisions faster.
See Also: Finance Services Review
