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5 Insurance Front-Office Processes You Can Improve with AI

6 minutes, 5 seconds read

Amidst the growing footprint of Insurtech around the world, Insurance service models continue to evolve for both front and back-office processes. Currently, InsurTechs are using AI in three main areas: Customer Experience (58%), Product Innovation (43%), and Process Improvement (19%) — according to a McKinsey report. An organization’s ‘Front Office’ strategy will need to embody intelligent sales force automation, call-centre management, help-desk applications, product configuration and risk assessment tools. Insurance Carriers are restructuring these operations with an outward focus — aimed at improving interactions with their customers. 

While the Insurance back-office is focussed on streamlining in-house operations, the front office is responsible for driving customer experience, engagement and behaviour. However, most front-office operations deal with repetitive customer-facing jobs. Using Artificial Intelligence-based technologies such as RPA, tasks that require human mediation can now be handed over to automation technologies that imitate human interactions. Gartner estimates 20% of RPA will be cloud-based by 2022.

The real benefit of undergoing automation transformation is that both the front & back office can now be contextually linked in a smart manner — avoiding ‘working in isolation’ for extended periods. Customer-facing agents and reps can access information across the back-end more reliably and faster than before. Automating even routine tasks such as updating customer information, performing security checks, fetching product details or updating complaint forms — can reduce resolution times and the potential for manual errors.

This allows the front-office staff to focus on the most pressing matter — the relationship with the customer.

Customer servicing can now take place at incredible scale and complexity using chat, mobile and voice self-service tools. For example, speech recognition can capture what type of service to offer the customer (eg: update contact information, access policy details etc). These tools can also detect ‘anger’ or ‘frustration’ from the tone of voice and the information is passed to front-line reps who can quickly resolve an issue. As a result, remote diagnostics and self-service tools will see enhanced adoption over the coming years. The market for AI-enabled technologies in the claims process alone will be worth $72B by 2020.

5 key front-office operations that can be improved with AI

  1. Underwriting
    The most central function within the insurance value chain is to price risk. Using AI, the insurance underwriting process is now empowered with real-time insights derived from models analysis tons of customer-centric data.

    Using historical data, machine learning models can be trained to understand ‘known risks’ based on experience. For ‘unknown risks’, IoT sensors play a crucial role — by delivering a real-time picture of an ongoing operation. This allows for a second model to infer risk based on current data and the entire historical record of that specific process.

    Armed with in-depth knowledge about risk, insurers are moving from traditional risk pricing to a more proactive risk mitigation role. Through this new approach, carriers can set up real-time risk alerts, predict fraud and more accurately forecast ‘claims occurrence’ across the customer life cycle.

  2. Policy Administration
    A policy administration system is a backbone that manages all the policies within an insurance company. From the first point of interaction to fetching data from the back-office — most, if not all core operations run through this system. However, most insurance organizations still rely on legacy systems that require tremendous workaround using manual efforts.

    According to a study by Celent, nearly 45% of Insurance CIOs identified disconnected and duplicative legacy systems as a key inhibitor to digital transformation.

    Today’s challenging market dynamics and competitive pricing pressures are changing this approach. There are several areas worth investing in for carriers such as image & voice recognition to capture and authenticate customer information at the initial contact stage to intelligent entity extraction tools for understanding even handwritten text from a physical document.

    Automation enhancements help drive policyholder retention by improving connectivity to the back-end and delivering the most optimal outcomes for front-office workflows.

  3. Claims Management

    Claims are the most widely scrutinized function within the insurance value chain. Most claims servicing is performed by human agents over the phone. With speech recognition, these conversations can be automatically transcribed/ translated in real-time. This frees up more agent time to handle greater issues while leaving automation enabled self-service to handle the most basic customer queries.

    Claims assessment or loss estimation itself can be performed remotely using image recognition tools linked to algorithms that can calculate the payout for the policyholder.

    Without the need for human intervention, straight-through processing can be dramatically improved by reducing processing time — allowing human agents to react faster to policyholders demands.

    Also, read – How AI can settle claims in 5 minutes!

  4. Marketing & Sales Distribution
    According to Salesforce, only 36% of the average salespersons’ week is spent selling. Human sales reps typically spend a large portion of their time nurturing unqualified leads. With sales funnel maximizers, like LCA, reps can get quick access to leads that have been scored, prioritised and allocated for the right agent to optimize conversions.

    Distribution and sales chains are moving to a completely digital and affinity-based ecosystem. Chatbots and virtual agents can, therefore, play a critical role in increasing cross-sell and up-sell opportunities. These AI-enabled tools are fitted with Natural Language Processing (NLP) capabilities to contextually interpret the interaction with the customer.

    AI also leverages predictive analytics to produce behavioural insights when pitching the customer — allowing the agent to ask the right questions, address unmet needs and resolve anticipated near-term challenges.

  5. Product Personalization
    Using Machine Learning algorithms to precisely price risk, allows Carriers to understand the complexities involved in new product development — especially measuring the ‘unknown risks’ involved in creating new product lines.

    Data (both historical and IoT derived) coupled with predictive analytics can offer more personalised guidance to insurance buying. InsurTechs are poising themselves strategically in this area, ahead of the large carriers, to attract a new and younger customer base. Companies like MetroMile, Trov and Lemonade have been able to create unique offerings with AI-derived insights fine-tuned to the individual, while also charging much lower premiums than the market.

    New customers are able to buy convenient, sachet-type, even pay-as-you-use modelled insurance products for protecting their assets (mobile, laptop, home appliances, short travel, vacations etc). This has brought about an appetite for on-demand insurance where insurance can be bought, queries can be resolved and claims can be processed, all within a few minutes.

Other Customer-Facing Areas improved by AI

1. Proactive Front-Office Processes 
2. Precise Risk Mitigation/Active loss prevention
3. Chatbots and Robo-advisors 
4. Real-time Underwriting 
5. Accurate Claims Processing 
6. Direct Marketing & Cu0stomer Retention
 7. Bespoke Insurance Advice
 8. Understanding User’s Emotions 

Forrester predicts the impact of intelligent automation — through evidence in ‘the service desk’. They claim: automation will eliminate 20% of all service desk interactions, by the end of 2019. Enabling human workers with digital assistants in the insurance front-office has scope for very high disruption. Human agents are prone to making repeat errors that automation equipped with AI can fix easily — especially in routine and repetitive tasks.

Carriers, now have the opportunity to boost their market position by improving agent productivity, reducing operational inefficiencies like reprocessing, producing errorless transactions for customers and thereby creating an uninterrupted service chain.
Mantra Labs solves the most challenging front & back-office operations plaguing the Insurance value chain. To know more about our work in this space, reach out to us on hello@mantralabsglobal.com.

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Open Finance: Reality or Hype?

3 minutes read

Open Banking has reshaped the fintech industry. Customers want a seamless experience with more convenient and flexible access to services. Technological innovation and digital transformation have led to the emergence of neobanks which offer a banking experience similar to delivery apps. Now the customers can avail of services like opening an account in minutes. In the last few years, another new concept- Open Finance has joined the queue. What exactly is open finance? Is it just hype or reality? And how open finance might improve customer experience (CX). These are some of the questions that we’re going to talk about in this blog. 

Open Banking

In open banking, banks and other financial institutions allow third-party financial service providers to access the bank’s customers’ data via APIs (application programming interfaces). This helps banks to create more personalized offerings and meet the changing needs of their customers.

What is Open Finance?

Open Banking and Open Finance are similar. However, Open Finance is slightly more advanced in the process. Simply put, it is the next step in open banking. 

Open Finance is a more customer-centric approach. It gives users a safe and dependable way to share their data with the financial tools and apps they prefer to use.

How is Open Finance different from Open Banking?

How is Open Finance different from Open Banking?

Source: Accenture

Open Banking has certain limitations when it comes to sharing of financial data. Here, only that data can be shared which is related to financial operations made within the bank’s app or in a branch office. Open finance goes beyond this limitation.

In Open Finance, non-banking financial data including mortgages, savings, pensions, insurance, and consumer credit – basically your entire financial footprint – could be opened up to trusted third-party APIs if you agree.

Open finance will help open new gateways for financial institutions to improve CX. Let’s dig deeper to understand how this concept will change CX in the Fintech world for the next-Gen customers. 

  1. 360-degree Customer Insights: Data acts as a tool to study deeply about your customers. Organizations can analyze the customer data and extract some valuable insights to design the complete customer journey. Open Finance opens a more secure pathway for financial institutions and gives a more complete picture of their customer’s finances. 
  2. Partnerships & Collaborations: With open finance, comes an opportunity for the financial institutions to network and collaborate with various providers. This means they could deliver a wider variety of services based on consumer data, uncovering new business models and innovations.
  3. Transparency for the Lenders: Lenders can evaluate and measure the creditworthiness of potential borrowers, audit documents, and offer customized solutions by securely collecting customer data. Machine learning algorithms may help to extract valuable insights from raw data.

Open Finance offers freedom and flexibility to consumers giving more options and control over the data they share and how they engage with their finances. With just 8 seconds of attention span, the new age consumers want better experiences to get hooked to one brand. Open finance creates unparalleled access to a broader range of products and services. With data sharing, banking organizations can keep track on the changing customer expectations who want frictionless interactions and hyper-personalized experiences across all touchpoints of the customer journey.

The Road Ahead

Statista predicts that there will be 63.8 million open banking users globally by 2024, increasing at an average annual rate of about 50% between 2020 and 2024. This means there will be more demand for innovative products and services in the industry. Banking organizations would need to analyze the rising customer expectations more closely than ever. And for this, data would act as a key to designing the experience of tomorrow. 

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