The financial services industry has never faced as many challenges as they do today. With increasing regulatory pressure, decreasing
margins, an increasing need to focus on the individual customer plus global competition, the industry is being forced to leverage
leading edge technology and processes.
Leading global financial services institutions, banking associations and lending organizations leverage Angoss predictive analytics
across credit risk, collections, fraud and marketing.
Rising costs of managing claims and risk combined with the struggle to increase profitability are some key challenges in the insurance industry. Property, casualty, health and benefits insurers have limited underwriting and investigative resources to analyze, review, assess and pay claims. The industry also faces competitive pressures to build new business, retain current customers and meet customer satisfaction requirements.
Using the wealth of data that resides in their various transactional and operational systems, combined with predictive analytics,
they can place themselves on the leading edge of insurance technology and address key business issues.
In the highly competitive telecommunications market, companies are competing for market share and revenue while balancing their appetite
for risk. Getting the right customers – the most profitable customers are key to revenue growth.
The challenge most telecommunications companies face are not data availability – but data overload. Angoss analytic solutions help you sift
through the data and apply intelligent advanced models to deliver precise marketing and credit strategies that will help you acquire and
retain the most profitable customer base.
Retail is one of the fastest growing industries. New trends are emerging and competition is increasing, especially in the online market.
Consumers are constantly sharing ideas and opinions with friends and the general public through numerous online platforms.
All this new online communication that both consumers and retailers access on a regular basis is creating even more data for retailers to store.
It is time to extract valuable information from all the existing data in order to meet customer demands, increase sales, improve business
performance and become a stronger competitor in the market.
As the market matures, mutual fund companies face a landscape where similar products are available from many competitors.
The implication is that, in order to win, companies must now differentiate themselves through superior distribution.
Predictive analytics leverage an organization’s business knowledge by applying sophisticated data analysis techniques to enterprise data.
As a result, mutual fund companies are able to build optimal strategies for customer acquisition and retention, upsell and cross-sell,
fraud detection and performance improvement.