In the digital age, it becomes important for companies to understand user behavior and needs to offer personalized services for better customer satisfaction. One key aspect is to leverage data assets and machine learning techniques to know more about consumers and products, which will help grow business, increase consumer satisfaction, and optimize operational cost.
In Gartner’s Top 10 Strategic Technology Trends for 2017, artificial intelligence (AI) and machine learning promise to be the future. While machine learning provides the backbone of automated decision making, AI helps a company build user-friendly computerized interfaces to interact with the customers. It is the world’s leading research and advisory company.
Based on the report, creating intelligent interactive systems that can learn and adapt will be the primary battleground through at least 2020. Organizations should leverage artificial intelligence and machine learning to address specific business scenarios in order to drive digital transformation.
Here are just some of the ways we are seeing machine-learning techniques drive business transformations.
Digitizing Fraud Detection & Claims Processing
One of the key issues in all the financial firms is fraud detection. With the help of machine learning, companies can build extremely accurate predictive models to identify likely fraudulent activity. This enables organizations to automate claims processing by differentiating a legitimate claim from a fraudulent claim, hence driving costs down by reducing human effort. This lowers costs with the same or better results and increases customer satisfaction.
Insurance Price Optimization by Predicting Loss
Machine learning techniques can create models to optimize price by predicting loss accurately. Accurate models also provide valuable insights to actuaries and underwriters. Accurate easy-to-understand models also help companies to examine whether the risk is priced appropriately.
Personalized Digital Banking Experience
Banks seek to deliver a personalized digital experience that helps their customers manage daily transactions and achieve their financial goals. Banks should leverage their data assets and machine learning techniques to understand customer needs. This understanding can help banks to provide customers with a personalized digital experience and improve their customer retention rates.
Robot Financial Advisors
A robot advisor engine can support portfolio management by providing clients with the right opportunities to match their financial profile. This engine can easily integrate with technologies like Alexa and Cortana to deliver exceptional investor/advisor experience. Such an integration will enable financial institutions to acquire new prospects and customers as well as retain existing customers. According to
Citigroup, Schwab estimates that the U.S. market potential for robo-advisors using machine learning in finance will be worth $400 billion in the coming years.
Improving Digital Traveler Experience
In this digital age, every business is focusing on personalization using recommendation engines to give the right offer at the right time. Using machine learning, customers can get an improved experience through proactive offers for services and products based on their preferences. Organizations can design travel assistants (chatbots) using natural language processing (NLP), which allow customers to create their own experience without any human interventions. Gartner predicts that 25% of customer service and support operations will rely on virtual assistant technology by 2020.
Predictive Maintenance for Airlines, Transportation, and Manufacturing
Predictive maintenance enables companies to predict failure and execute corrective actions or even replacement of systems. Sensor data form airplanes, trucks, machines, etc. can be leveraged to monitor current conditions and predict future failures, leading to optimized periodic maintenance operations, major cost savings, and increased availability of the equipment. GE Aviation has made significant investments in predictive maintenance by leveraging the power of Big Data, Internet of Things (IoT), and machine learning technologies.
As organizations embark upon their digital transformation journey, tools and technologies like AI and machine learning have a significant role to play and should be an essential part of the technology roadmap. Such a roadmap enables organizations to develop focused strategies to leverage for increased financial benefits and reduced risk.
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