Consumer sentiment edged down 4% in January, marking the first decline in six months, according to the final results for the University of Michigan’s January Surveys of Consumers.
“While assessments of personal finances inched up for the fifth consecutive month, all other index components pulled back,” Surveys of Consumers Director Joanne Hsu wrote in a Friday (Jan. 24) press release. “Indeed, sentiment declines were broad based and seen across incomes, wealth and age groups.”
Consumers’ concerns about unemployment and inflation drove the declines, according to the release.
The share of consumers said they expect unemployment to rise over the next year — 47% — was the highest since the pandemic recession, the release said.
Year-ahead inflation expectations rose to 3.3%, a reading that was the highest since May and above the range of 2.3% to 3% that was seen in the two years before the pandemic, per the release. Long-run inflation expectations rose to 3.2%, which was the same reading as November.
“For both the short and long run, inflation expectations rose across income and education groups,” Hsu said in the release. “Concerns over the future trajectory of inflation were visible throughout the interviews and were tied to beliefs about anticipated policies like tariffs.”
Consumers mentioned that they were buying goods now to avoid expected future price increases, according to the release.
“January’s data closed on Inauguration Day, and consumers of all political leanings will continue to refine their views as Trump’s policies are clarified and implemented,” Hsu said in the release.
It was reported Jan. 3 that the price of new cars could rise due to tariffs that President Donald Trump has threatened to impose on Canada and Mexico — two countries that play important roles in the U.S. automotive industry’s supply chain.
Trump has said that tariffs can offset what he argues are unfair practices from foreign companies and governments.
JPMorganChase CEO Jamie Dimon said Jan. 12 that when used properly tariffs can be helpful with issues like national security and unfair competition.
“Like any tool, if it’s misused it can do damage too,” Dimon told CBS News, noting that he hadn’t spoken with Trump on the matter.
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.”
