The Use of Analytics in Finance
retailciooutlook

The Use of Analytics in Finance

By: Retail CIO Outlook | Monday, June 17, 2019

FREMONT, CA: The rise of advanced analytics has urged the financial services to invest in their resources in the troves of big data and improve the revenue potential. The emergence of data services which offer structured, machine-readable data and unstructured data such as video, text, and posts on social media have unlocked new doors for financial services.

Major financial institutions such as banks are starting to rely on media data, evaluating public behavior on social platforms, to recognize credit risks. The insurance industry, which has leveraged data and analytics for the last few years in underwriting, claims, fraud detection, and so on, is seeing the benefits of their investment. The transition from batch to real-time processing and visualization of data feeds is transforming the core operations in claims, billing, and CRM.

The application of advanced analytics has fueled a wave of innovation across the financial sector. Auto insurers are incorporating behavior-based credit scores into analytics to assess the consumers. Also, they are leveraging agent characteristic and behavioral data to predict their selling and marketing potential. Several insurance organizations are implementing cutting edge machine learning models to make accurate predictions of agent and advisor success, relying more on submissions based on market segmentation, advanced data, and deeper insights.

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Data analytics is also used to manage lapse rates by utilizing geospatial, household, and zip code level demographics data to reduce customer churn. The financial organizations are investing in analytics more than ever, paving the way for data revolution within the sector. The gradual implementation of innovative use cases and business models will potentially enhance the core functions of financial services.

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Creating insights will enable the organizations to develop enhanced models which can offer better yield value. As the analytics matures, the model developers can collaborate with the frontline staff to create more effective analytical strategies that can bolster the decision-making ability of the organization.

Analytics is becoming the backbone of financial processes, deciding the business process, and the core strategies. It not only enhances underwriting, product development, and distribution but also helps in breaking down the barriers between functional silos. As the analytical system becomes more sophisticated, financial services are focusing their strategies on identifying, recruiting, and retaining analytical skills.

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