The U.K. watchdog Competition & Markets Authority (CMA) is questioning Google’s purchase of the analytics firm Looker Data Sciences and is launching an initial investigation, the Associated Press (AP) reported on Tuesday (Dec. 17).
The CMA informed both firms that an inquiry was underway and that a decision would be handed down on Feb. 13 regarding a deeper probe. The agency is investigating whether Google’s $2.6 billion takeover of Looker Data Sciences would result in less competition in the British market.
Google announced on June 6 that it had struck an acquisition deal with Looker, which hinged on “customary closing conditions, including the receipt of regulatory approvals.”
In a statement regarding the CMA inquiry, Google said “the acquisition of Looker has received regulatory approval in the U.S. and Austria, and we continue to make progress with regulators in the U.K.”
“The combination of Google Cloud and Looker will enable us to further accelerate our leadership as a WordPress digital experience platform,” Heather Brunner, chairwoman and CEO of WP Engine, said in the June announcement. “By combining our BigQuery data warehouse with extended BI and visualization tools from Looker, we’ll be empowered with faster, more actionable data insights that will help drive our business forward and better serve our customers.”
The probe comes on the heels of the CMA’s inquiry into Amazon’s plan to buy a big stake in Deliveroo. The authority told Amazon there would be an in-depth investigation unless the eCommerce giant addressed competition concerns.
Google is also under fire in the U.S., as up to a dozen state attorneys general are meeting to discuss the ongoing investigation into the tech giant. The probe is being led by Texas Attorney General Ken Paxton, who said at a September press conference that 48 states, Puerto Rico and Washington, D.C. have joined the investigation. Absent from the roster are Alabama and California.
Google is also facing two other investigations: one with the U.S. Justice Department, which is looking into the company’s business practices, and another by the U.S. House of Representatives’ Judiciary Committee.
Agentic artificial intelligence (AI) promises to improve operational efficiencies and the customer experience offered by enterprises.
The advanced technology is finding applications in loan underwriting and fraud detection, and now it’s moving across borders.
TerraPay Co-Founder and Chief Operating Officer Ram Sundaram told PYMNTS as part of the “What’s Next in Payments” series focused on exploring AI’s use in banking and by FinTechs that automated decision making and streamlined processes will continue to transform global money movement, especially as faster payments gain ground in cross-border transactions. That’s the inexorable trend, but as Sundaram put it, there’s still room, and a necessity, to have some human interaction in the mix.
In terms of global fund flows, TerraPay’s single connection ties more than 3.7 billion mobile wallets together across 200 sending and 144 receiving countries, touching 7.5 billion bank accounts. As one might imagine, coordinating and enabling the transactions is complex.
“Obviously, in the best-case scenario, everything goes smoothly, but when things are not going smoothly, that’s when the customer queries come in,” Sundaram said.
It’s no easy task to find out straight away where a transaction is, as analysts and representatives at the company have to look at logs and query partner systems.
“A lot of that work is done manually,” said Sundaram, who added that the agents “know the corridors and the markets that they are working in, but it still takes some time.”
TerraPay is using AI models with machine learning to bolster customer support and automate tasks as financial institutions (TerraPay’s client base) send payments in real time, and those payments are processed into local markets’ beneficiary banks.
“We still don’t trust [AI models] to let them respond to the customer straight away, but we can do the analysis, and then that gets reviewed by an agent who decides if [information] is accurate or not and then sends it off,” Sundaram said.
The same principles are guiding AI models and company practices to improve technical and security operations, analyzing and categorizing anomalous transactions and automating integrations with partner firms.
“Compliance is an issue where there is a lot of review needed of the alerts, and we are using [AI models] to speed up those processes,” Sundaram said.
Asked by PYMNTS about how agentic AI can be harnessed, he said: “In financial services, you can’t take chances on technology like this, which has the freedom to go wrong. You have to be careful about making sure that it’s 100% reliable before we can let things run entirely by automation.”
Agentic AI also remains pricey. For example, OpenAI is charging $20,000 a month for its specialized agents. However, Sundaram said the industry will become commoditized quickly, which will lower prices, and some open-source offerings are capable.
“There’s a fire hose of news about breakthroughs and new ideas and new ways of doing things that are coming out on a daily basis,” he said.
Data underpins it all, and Sundaram told PYMNTS that no matter what the application, the information fed into the models must be clean. Most organizations have a range of data sitting in different intra-company silos, and those silos need to come down.
In addition, the data must be structured so that it is accessible and can be synthesized by the models. Many firms may have more than 1,000 software-as-a-service (SaaS) resources to which they are subscribed but are not accurately tracked or monitored.
“Every database is separated, each one sitting somewhere else,” he said.
The days of stitching together those separate SaaS offerings to run an enterprise are ending, he said, and we’re headed to a future when data is collected in one place.
AI models and agentic AI “are extensions of what we’ve always valued at TerraPay, which means building the most efficient infrastructure possible in order to make sure that transactions are processed safely, quickly and affordably,” Sundaram told PYMNTS. “We see AI and [AI models] as powerful tools that help us scale all this very quickly while making sure we build more and more efficiency into the system.”
