“Change is not only likely, it’s inevitable.” – Barbara Sher What is Data Shift or Data Drift? Given human nature, it is very natural that the data we collect will change over time. Changes in data such as behavior and preferences are fast and drastic. It is even more relevant today with the impact of the Covid-19 pandemic making unprecedented changes to businesses. For a data science practitioner, the stability of data and its source are salient to develop and maintain robust ML (machine learning) solutions. Changes or drift in data will degrade the performance of predictive models.
In recent years, Machine Learning (ML) algorithms have advanced and are now capable of learning accurate and complex patterns provided large and labeled data samples are available. However, many ML implementations fail to generalize when new data points are encountered, especially data points with different and unseen patterns or conditions from training samples.
The insurance industry in India is expected to reach US $280 billion by 2020-2021. The life insurance industry is expected to increase by 14-15% annually during the next three to five years. With such rapid growth in the life insurance market, the number of fraud casess is also expected to increase. Recent reports suggest that fraud consume more than 8.5% of the revenue that the industry generates.
As we set our feet in 2023, having experienced a roller-coaster ride last year thanks to the geopolitical tensions and some lingering rub-off effects of COVID-19, it drives home that "change is the only constant." Like any other industry, insurance is undergoing paradigm changes at different levels, whether recruiting potential candidates or customer onboarding, to name a few. However, a common thread that ties the myriad business functions of an insurance company has been data and innovation. There has been an ever-increasing need for insurance providers to use data and embrace innovation in their routine activities, eventually to stand the cut-throat competition.
Risk Management is a core function within the insurance industry. It is a vital responsibility of the underwriting team. Insurance companies collect data scattered across different business units in various formats – some of which are paper and digital, most of which are typically unstructured. The underwriting team doesn't have immediate access to the information required for internal and external decision-making, resulting in delays in making decisions and costly mistakes.
Most insurers offer similar products and services, which makes it challenging to attract new customers and retain them. As an industry, insurance is low-touch, and insurers seldom interact with their customers. A report shows that the top companies have an average customer retention rate of 93 - 95 percent, while insurance companies have an average of 84 percent.