Client – Multi-Asset & Macro (MA&M) are in the process of developing strategies that can be used in existing products, decision tools that can help in managing existing portfolio risks, and also the development of new products and solutions that combine both. The desired outcome is to optimize existing products as well as developing additional strategies that seek to exploit non-traditional risk premia such as value, momentum, or carry-based strategies. This work is currently being undertaken by various team members across MA&M. However, the work is generally siloed, manually intensive, and time-consuming, performed in an unstable environment, is not scalable in its current form, and cannot utilize the multiple, large data sets required. Additionally, there is no current resource available to store this data as a time series, which frustrates further avenues of analysis to be done. The situation demanded the need for an integrated platform for data scientists equipped with accelerators and tools to experiment, discover, share, and deliver insights. The xpresso.ai platform brings together best-of-breed tools into one integrated and intuitive platform.
An American multinational consumer credit reporting agency and is one of the three largest agencies. They collect and aggregate information on over 800 million individual consumers and more than 88 million businesses worldwide to create insights that help organizations make more informed decisions. The mortgage is one of the industries our client serves. They provide mortgage lenders with a 360-degree view of a borrower’s credit, capacity and collateral. Risk models eventually become less predictive or relevant due to evolving market conditions.
As early as the 1990s, AI researchers had speculated that AI would change the forex market. A groundbreaking article called Managing Foreign Exchange for Competitive Advantage published in the MIT Sloan Management Review underlined the importance of computerized models, how they’d become indispensable in the foreign exchange market, and by extension, trading exchanges. Traders are increasingly relying on predictive analytics, and sophisticated big-data AI algorithms are capable of real-time data collection and eventually making accurate forecasts.
Risk management in banking means figuring out ways to deal with potential losses. Risk management usually focuses on managing a financial institution’s exposure to losses or risk. It also tries to protect the value of its assets. Banking can be broken down into many different types. However, this focuses on traditional banking and trading activities. Overall, banking activities create many unique risks related to a bank’s credit, liquidity, trading, revenues and costs, earnings, and solvency issues.
Sentiment analysis involves indexing social media comments and using algorithms to determine whether they are positive or negative. By combining NLP (Natural Language Processing) and ML techniques, a sentiment analysis system for text analysis reads different texts from a variety of sources. It then stacks them against a sentiment library (a repository of adjectives that have been assigned a sentiment score based on usage and feedback from readers. It then assigns weighted sentiment scores to the entities, topics, themes, and categories within a sentence or phrase.
The property and casualty (P&C) insurance industry is being rapidly transformed by AI. Improved computing power in the cloud, cutting-edge algorithms, and big data are some of the tools that make implementation easier. These are some of the key tenets that are helping insurers achieve lowered costs. By adopting AI-powered digital systems, data is collected, aggregated, and analyzed. As more data is collected over time, the AI systems learn the rules of the business. This helps the AI system improvise the involved processes and helps optimize them.
Although insurance is an old industry and remains highly regulated, insurance companies are recognizing AI-driven solutions that can augment their technological capabilities so that their business can become leaner, faster, and more secure. AI has the potential to transform the insurance experience for customers from frustrating and bureaucratic to fast, on-demand, and affordable. Insurers are waking up to the idea of using cutting-edge AI to find, harvest, and analyze data from both the surface and deep web held across millions of academic papers, patents, government reports, databases, journals, and news items. It will be used to find signals and generate trends that can help businesses make important decisions about the future.
Underwriting is a critical business because it involves assessing potential risks that the insured is exposed to. It is why underwriters determine the extent of the coverage and the price the consumer is entitled to. They also decide whether to approve the insurance policy. While adding a new policy to their ledger, an underwriter takes on the risk. Because anything can happen in life, there is a lot of uncertainty around whether a claim gets filed or not. Many factors influence the risk of an individual.
Consumers have now come to expect modern solutions from banks that can use their access to consumer data to create excellent credit card recommendations and other financial products. Banks can also use this information to make quicker decisions and reduce fraud. They can use predictive analytics to drive operations.
For most, access to credit is an essential requirement as it depicts their financial success and predicts their upward mobility. Today, a favorable credit rating is a must for those who wish to…