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Insurance accounting and operations face many obstacles when adopting technology to solve the problem of big data. AI technologies can help clear these data hurdles, but clunky legacy systems, manual processes and change management often stand in the way. Without a clear path to innovation, investment operations teams remain overburdened by data, which distracts them from developing long-term operational strategies and other higher-value activities.
Big Data, Big Distractions
Today’s investment opportunities and regulatory environment demand more data from insurers than ever before. Middle-office teams face rigorous requirements for reporting accurate, comprehensive information about investments.
Legacy systems commonly used across the industry simply can’t keep up. As a workaround, data is extracted from various systems and cobbled together in spreadsheets for further analysis and reporting. Workflows are tedious, manual and time intensive, not only creating headaches for IT and the middle office, but also concern from regulators about data integrity. Investment accounting and operations soon find themselves with a serious data and technology problem on their hands.
How Can AI Solve the Big Data Problem for Insurance?
A key obstacle for IT teams is to clear the path for innovation. Investment operations isn’t always considered equally crucial to insurance functions as investment performance. While the insurance industry may see less imperative for adopting new middle-office technology, the problem it solves for—big data—is a legitimate burden with serious consequences.
Robotics Process Automation
Robotics process automation (RPA) is the first type of AI technology that can help. RPA doesn’t require complete system overhauls or major disruption to IT infrastructure. RPA instead acts as a bridge across systems to automatically extract, aggregate, normalize and reconcile data. With little to no human involvement needed, RPA drastically reduces manual work and improves accuracy, flexibility, efficiency and speed—key benefits where regulators and the investment office are concerned.
The improved efficiency and speed RPA offers create less need to offshore operations functions. Investment accounting and operations maintain full control over data, enhancing access and quality while gaining more time to pursue strategic activities. Updating RPA technology is less complicated and handling errors becomes more efficient. Over time, the volume of data that can be managed quickly and accurately increases. This is why RPA is an attractive option for firms beginning to shift their technology approach.
Getting Started with RPA
The relatively low cost and IT disruption of RPA, compared with other AI technology, make it a common launch pad for insurance firms beginning to shift their IT strategy. Whether adopted internally or outsourced to a vendor, firms can start by using a proof of concept to deploy RPA for swivel chair processes that have predefined activities, such as:
- Repeatable activities that send, receive, collect and manipulate structured data
- Functions that produce high volumes of data and information
- Manual tasks where current costs are clearly understood
- Workflows that will yield the most concrete benefits from automation
- Error-prone tasks that contribute to large-scale quality issues
Even with large-scale technology transformations on the horizon, RPA is an attractive near-term solution because of its quick return on investment and low implementation cost.
Machine Learning and Natural Language Processing
Machine learning and natural language processing (NLP), like robotics, use logic to automate decision-making. Machine learning goes a step further by thinking about those decisions and learning from them to identify overall patterns in data. Over time, larger trends emerge that enable middle-office teams to make predictions and adapt to change faster. Less guesswork and analysis are needed to decipher trends – a common difficulty when working with structured data in spreadsheets or relational databases.
While machine learning typically focuses on structured data, NLP reads, understands and applies context to unstructured data, such as text, images, PDFs and websites. NLP frees up the time needed for higher-level thinking. Instead of pouring over thousands of documents, accounting and operations teams can spend time transforming data into information and insights, ultimately developing narratives that help make faster, more-accurate decisions in ambiguous situations with far-reaching consequences.
On the compliance and regulations side, machine learning and NLP offer the same key benefit as RPA: trustworthy data. Middle-office teams no longer need to rely on inaccurate or incomplete information. Duplicate processes are also eliminated, siloed information freed and human error reduced. Ultimately, a single source of truth emerges through AI that forms the basis of solid master data management and governance practices. Considering the data scrutiny placed on the financial industry, the promise of AI is a game changer.
Call It Augmented Intelligence Instead
The notion that AI replaces human workers is oversold. Technologies such as RPA, machine learning and NLP aim to complement human activities, not replace them. The longer accounting, operations, IT and middle-office teams wait to fix their tech problem, the longer they will play catch-up. Clearing the path to RPA and other AI innovations offers significant benefits above and beyond cost and efficiency: data governance, regulatory compliance, data security and operational risk. The single biggest benefit to insurance companies may be freeing up the valuable and scarce resources their people need to apply their insurance acumen to core insurance activities.