The value of cryptocurrency transactions is forecast to surpass $1 trillion this year, which is 15 times the level reached in 2016.
That’s according to a new study from Juniper Research called The Future of Blockchain: Key Vertical Opportunities & Deployment Strategies 2017-2022. In a press release highlighting results of the report, Juniper found transaction values in the first half of this year surpassed $325 billion, thanks in part to a huge jump in the price of Ethereum, which accounted for two-thirds of cryptocurrency transaction values at the time. Currently, cryptocurrency is seeing average daily trades that are well in excess of $2 billion. If the current levels are maintained, the daily trades could surpass $100 billion in transactions in 2017 alone, Juniper said.
In the case of bitcoin, the leading cryptocurrency, since the start of this year prices have increased from roughly $1,000 to $4,000. A second planned fork in November could split the bitcoin community and result in a decrease in the value of the coins, Juniper said. “There is no resolution in sight to the continuing and fundamental disagreements between many Bitcoin miners and Bitcoin Core developers over the future of the cryptocurrency. This in turn could lead to uncertainty about Bitcoin’s future and downward pressure on its valuation,” said research author Dr. Windsor Holden in a release focusing on the research.
Over the weekend bitcoin hit $5,000 late Friday (Sept. 1) but then retreated 5 percent in late trading, according to news from Fortune. At the beginning of the year, the possibility of bitcoin hitting $5,000 per coin was laughable, but with the price rising higher all year, it suddenly became a reality, albeit a brief one. There was no one reason for bitcoin to jump late last week, although Fortune speculated it is more of the same: speculation because of the popularity of cryptocurrency. Other reasons for the uptick according to Fortune include new financial products that are creating a lot of liquidity, increases in trading in Asian markets and institutional investors taking the cryptocurrency craze seriously.
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.”
