Beyond COVID-19; How to Leverage AI and ML to improve data quality

Beyond COVID-19; How to Leverage AI and ML to improve data quality
October 7, 2020 | Data Quality

Beyond COVID-19; How to Leverage AI and ML to improve data quality

In today’s fast-changing, big data world organizations that have understood the value of artificial intelligence have not only been able to keep up with their customers’ needs but to also predict future trends in customers’ preferences. However, when the COVID-19 pandemic hit the global financial systems, all models fell short in understanding which path their customers would be taking. However, after a few months of learning about COVID-19, organizations are now starting to predict how and when the world economies’ will recover. With the disruption that occurred in almost every economic sector leading to many employees working remotely or customers accessing the required services online for the first time, the quality of data received by organizations was negatively impacted. Part of the recovery process for most organizations should include the improvement of data quality by utilizing artificial intelligence to reduce cost and the time required.

Quick read: Why AI/ML based data quality makes sense?

Where to Leverage Artificial Intelligence and Machine Learning

Many businesses have been closed leading to many employees losing the jobs and an income they relied on to meet their financial obligations. Loan default levels by businesses as well as individuals are at an all-time high and banks are starting to feel the full impact of the COVID-19 pandemic. However, with the development of vaccines gaining momentum and the possibility of getting a reliable vaccine by early 2021, there is growing optimism that most industries will be thriving again as soon as the vaccine is made readily available for the public. Banks should be looking to leverage AI services in classifying the recovery pattern of different industries and use this information to reclassify their loan’s portfolio. Decisions such as loan repayment restructuring should be based on the industry’s speed of recovery. By understanding the individual’s source of income and their spending patterns in the past years, it will be possible to see the individuals whose income was temporarily reduced or halted by COVID-19; then, make a prediction on their financial position post-COVID-19. If this is done, it can allow the bank to customize their customer’s experience as well as significantly increase the chances of bringing in more revenue at a lower risk.

Secondly, with so many people unemployed and the financial pressure continuing to pile, there are more attempts by hackers and other unscrupulous people to commit fraud. Furthermore, many companies were forced to implement remote working without sufficient notice to set up or procure a secure system to handle sensitive information. This has led to sensitive information falling into the hands of fraudsters who are also looking for businesses to reopen. Many of the people whose sensitive information has been acquired while working remotely are not yet aware and this increases the need for banks to have AI systems set and ready to flag suspicious bank transactions. By obtaining the current information about their customers, the AI algorithms will have a higher chance of predicting which transactions are normal for the customers and which one suspicious.

With hundreds of thousands of lives lost and millions having been rendered jobless, the data that is possessed by financial institutions have lowered in its overall quality in terms of accuracy and consistency. For the pre-COVID-19 quality of data to be achieved, or even higher, at a faster rate, machine learning should be utilized to ensure the data being collected is accurate. Other AI algorithms, with access to third-party information, should be used to identify the accounts that need to be closed if the account holder is no longer alive. Supervised machine algorithms such as Support Vector Machine should also be employed to validate signatures of customers who show up to the bank. Banks must use the post-COVID-19 period to not only refine their data but to also improve it using artificial intelligence in order to make more accurate predictions.

The biggest advantage with artificial intelligence is that we learn from both negative and positive experiences and apply this knowledge in making future decisions. From businesses that experienced catastrophic losses that are eventually felt in the financial sector, to permanent changes in the way we interact with each other; artificial intelligence can learn from this and help customize products to meet the customers’ needs while still in the recovery process. It is therefore important that artificial intelligence is utilized as early as possible in a targeted and efficient way that promises maximum rewards.

DQLabs, AI-augmented Data Quality Platform

With many people forced to work remotely for the first time, it became a learning process full of trial and error. The availability of so many data entry points, with some being unsecured has posed a great risk to organizations that deal in big data and use this data to make critical decisions about product design and marketing strategies. Customers who would visit the businesses to transact also found themselves being encouraged to make their transactions online or via the phone. Organizations, especially in the financial sector, found it difficult to optimize their available labor force to meet unique customer needs at a time of crises. With the recovery process having already begun, it is important that these organizations employ artificial intelligence to harmonize the data received during the peaks Covid-19 restrictions to ensure it is complete, valid, accurate and consistent. Artificial intelligence can be employed to also isolate this data, analyze the employees’ performance during this period and identify possible training needs. This will allow the organizations to not only achieve employee development but also increase their preparedness in case of a future pandemic.

Organizations should also look into other areas where they can employ artificial intelligence. For instance, beyond the data analysis, organizations can utilize machine learning for remote working verification and monitoring. This can enable the organization to increase its ability to accommodate remote working which has proven to be a good way of reducing business operations cost while still maintaining the security levels which was available in an office environment.

Conclusion

In conclusion, artificial intelligence and machine learning should play a key role in the recovery process, future operations and in continuous risk assessment. The large amount of data available to organizations will provide a good opportunity for them to predict current and post COVID-19 trends if they harness the power of artificial intelligence. With the long term impact of COVID-19 yet to be understood, organizations’ artificial intelligence using should be at the forefront in understanding how customers’ needs shall be impacted.