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Express Cloudera POV on 2021 data trends in insurance

Cloudera

We’ve written about the changes forced on the traditionally risk-averse insurance industry by COVID-19. In 2021, with the crisis hopefully fading, insurance will have time to evaluate the changes made in 2020, assessing what worked and what didn’t, and planning a new way forward rather than reacting in real time. .

Insurance 106
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Interview with: Sankar Narayanan, Chief Practice Officer at Fractal Analytics

Corinium

I am the Chief Practice Officer for Insurance, Healthcare, and Hi-Tech verticals at Fractal. The Insurance practice is currently engaged with several top 10 P&C insurers in the US, across the Insurance value chain through AI, Engineering, Design & Behavioural Sciences programs.

Insurance 250
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What your CFO really needs in periods of economic uncertainty

CIO Business Intelligence

growth underscores how inflation, interest rate fluctuations, and consumer spending are reshaping forecasts, investment portfolios, and the CIO agenda. Regardless of your company’s investment posture during this period of instability, interactions with the CFO have likely increased and become more consequential in the last few months.

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NLP Isn’t Enough. Leading Financial Services Companies Are Now Moving to Conversational AI.

CIO Business Intelligence

Today, most banks, insurance companies, and other kinds of financial services firms have deployed natural language processing (NLP) tools to address some of their customer service needs. Juniper Research predicts that chatbots will account for 79% of successful mobile banking interactions in 2023. How is conversational AI different?

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Why Financial Services Firms are Championing Natural Language Processing

CIO Business Intelligence

In business, when a trend is forecast to grow by more than 3000% and generate cost savings of $7.3 Those numbers represent the projected growth of chatbot interactions among banking customers between 2019 to 2023 and the cost savings from 862 hours less of work by support personnel, according to research by Juniper Research.

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Conversational AI use cases for enterprises

IBM Big Data Hub

The emergence of NLG has dramatically improved the quality of automated customer service tools, making interactions more pleasant for users, and reducing reliance on human agents for routine inquiries. These technologies enable systems to interact, learn from interactions, adapt and become more efficient. billion by 2030.

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PODCAST: COVID19 | Redefining Digital Enterprises – Episode 7: The Impact of COVID-19 on Financial Services & Risk Management

bridgei2i

Additionally, institutions are finding it difficult to forecast trends, as historical data isn’t relevant anymore. I can’t exactly forecast my losses, cross sell and growth hence becomes tougher decisions to make. Understand loss forecasts adjustments, that needs to be made, plan for collection based on emerging patterns.