#data aggregation

“While Israel struck Iran and the United States moved in behind them, while every newsroom in the country was [rightly] covering one of the most consequential moments in years, Oracle Corporation quietly published a blog post announcing that the U.S. government had authorized it to run generative AI on federal government data. That means pattern recognition, automated data analysis, and decision-making tools now approved to operate on your Medicare records and military systems, cleared at the highest civilian and Department of Defense security levels. (And yes, Oracle also just took over the infrastructure running your Medicaid and ACA data — that happened this week too, and we’ll get there.)

EU and UK open antitrust probe into Google and Meta over online ads (March 11, 2022)

Regulators in Europe and the UK have opened an antitrust probe into a deal between Google and Meta on online advertising, in the latest effort to tackle the market power of the world’s biggest technology companies.

The move follows US antitrust investigators who are also probing an agreement informally known as “Jedi Blue.” The search engine giant and Facebook’s parent company have been accused of working together to carve up advertising profits, acting together to buttress their businesses.

“Advertising is very important and because of that, it is important that there is competition on who can place ads where,” Margrethe Vestager, the EU’s competition chief, told the Financial Times. She said the UK and the EU had agreed to open the probe into the matter on the same day. “There are other people than Google doing it. What we suspect here is that there may have been an agreement between Google, and then Facebook, only to use Google services and not competing services. That’s a giant problem.”

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As financial institutions navigate an increasingly complex global market, the necessity for robust risk governance frameworks has never been greater. Automated risk governance represents a pivotal evolution in enterprise risk management, offering enhanced capabilities for institutions to mitigate potential threats while ensuring compliance with stringent regulatory demands.

By embracing Automated Risk Governance, banks can streamline crucial functions such as credit risk modeling and VaR calculations. These advancements facilitate accurate assessments of Expected Credit Loss (ECL) and Loss Given Default (LGD), creating a transparent view of financial health and risk exposure.

Enterprise Risk Data Aggregation

Effective governance hinges on comprehensive risk visibility across all levels of the organization. Establishing a central data repository that aggregates risk metrics empowers institutions to conduct real-time risk assessments with greater accuracy. This practice fosters an adaptive response mechanism to emerging market dynamics.

Improving Liquidity Risk Management

The importance of liquidity risk management cannot be understated, particularly in volatile market conditions. Implementing automated systems that monitor liquidity coverage ratios (LCR) and net stable funding ratios (NSFR) enables institutions to maintain operational stability while optimizing their collateral frameworks. AI-powered solutions deliver agility and precision in maintaining financial endurance.

Conclusion

 A commonly known example of data aggregation is the Consumer Price Index (CPI) from the Department of Labor which aggregates price changes in a wide variety of goods and services to track the fluctuation of the cost of living in the U.S. Unfortunately, despite the importance of data aggregation and its potential to improve decision-making, organizations still make major data aggregation mistakes. 

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Aggregated data is extremely valuable in a world with ever-increasing regulations on data sharing and security. That’s because aggregate data is anonymized and doesn’t carry the same restrictions or consent obligations as personal identifiable information (PII).

This makes it easier to share, which can be vital in fields such as healthcare. However, a common data aggregation mistake is falling for the illusion of less risk, leading to oversharing and data breaches. To overcome this, data administrators need to have greater access control over all data sharing to ensure even aggregated data never leaves their control.