AI carries security risks in banking, including being used by scammers to target financial information. Photo via Getty Images

With artificial intelligence technologies easily accessible and growing in popularity, consumers and business owners alike should be aware of both the benefits and risks when it comes to the utilization of generative AI tools in banking and finance. While data-driven AI creates the opportunity to further drive innovation in banking, the data-reliant nature of the industry makes it a natural target for scammers looking to intercept personal and business finances and sensitive customer information.

As banks and other financial service providers are using AI as a tool to scan for anomalies or errors that are known fraud techniques, criminals are using AI to improve their chances of perpetrating fraud. For this reason, consumers and businesses should guard their data with the same diligence used to guard cash and other valuable physical property.

Privacy and accuracy

For entrepreneurs and businesses of all sizes, it is important to keep in mind the practical applications of AI beyond the trending headlines, whether implementing the technology into everyday internal business practices, or into client-facing solutions.

When feeding information into AI, it is best to maintain a defensive position and be proactive about not disclosing sensitive or private information. Also, rely on sound judgment when deciding when and how to use AI technologies. From a business standpoint, privacy should be embedded into a financial system’s design and leaders should be transparent about the technologies used within a given system.

Technologies like ChatGPT are large language models operating on massive datasets, including documents and web pages across the internet. This poses a risk because some sources of this data lack accuracy. When seeking financial advice via AI technologies, it is best to conduct research by curating and limiting the dataset then talking through your unique financial position in person with your trusted banker and IT staff or consultants.

Phishing and business email compromise via AI

Historically, phishing and business email compromise, or BEC, attempts have been more easily recognizable and often flushed out due to grammatical errors and unnecessary punctuation. With technologies like ChatGPT, scammers are now better equipped to draft well written content that can fool a person into thinking a communication is legitimate. Phishing can lead to people clicking links or attachments that harbor malware or other viruses that can lead to account takeover. With BEC, a person might be fooled into thinking an email is from a legitimate person. Scams like these could potentially lead to the disclosing of sensitive information or accepting transaction instructions or changes, ultimately resulting in money being sent to a fraudster.

AI voice generators

AI voice generators can be used to mimic voices of anyone including bankers, C-suite leaders and customers. If a person is fooled into believing they have received a voicemail or are talking to a person they know, they may accept instructions from a fraudster like providing transaction approvals and sensitive or private information, resulting in fraud.

AI can also create fake identities, including AI-developed photos of individuals, and other false information. These fake identities could be used to create accounts for fraudulent purposes.

AI is here to stay

AI is forecasted to have a lasting impact on the banking industry. Whether on the business or consumer side of the spectrum, it will be important to embrace the innovation and enhancements generative AI will continue to produce, while maintaining a cautionary stance around protecting client and business information and finances. Fraud prevention practices will need to continue evolving alongside the fast-paced growth of generative AI in banking.

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Ken Smiley is treasury management division manager of Amegy Bank and a fraud protection expert.

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Houston researcher builds radar to make self-driving cars safer

eyes on the road

A Rice University researcher is giving autonomous vehicles an “extra set of eyes.”

Current autonomous vehicles (AVs) can have an incomplete view of their surroundings, and challenges like pedestrian movement, low-light conditions and adverse weather only compound these visibility limitations.

Kun Woo Cho, a postdoctoral researcher in the lab of Rice professor of electrical and computer engineering Ashutosh Sabharwal, has developed EyeDAR to help address such issues and enhance the vehicles’ sensing accuracy. Her research was supported in part by the National Science Foundation.

The EyeDAR is an orange-sized, low-power, millimeter-wave radar that could be placed at streetlights and intersections. Its design was inspired by that of the human eye. Researchers envision that the low-cost sensors could help ensure that AVs always pick up on emergent obstacles, even when the vehicles are not within proper range for their onboard sensors and when visibility is limited.

“Current automotive sensor systems like cameras and lidar struggle with poor visibility such as you would encounter due to rain or fog or in low-lighting conditions,” Cho said in a news release. “Radar, on the other hand, operates reliably in all weather and lighting conditions and can even see through obstacles.”

Signals from a typical radar system scatter when they encounter an obstacle. Some of the signal is reflected back to the source, but most of it is often lost. In the case of AVs, this means that "pedestrians emerging from behind large vehicles, cars creeping forward at intersections or cyclists approaching at odd angles can easily go unnoticed," according to Rice.

EyeDAR, however, works to capture lost radar reflections, determine their direction and report them back to the AV in a sequence of 0s and 1s.

“Like blinking Morse code,” Cho added. “EyeDAR is a talking sensor⎯it is a first instance of integrating radar sensing and communication functionality in a single design.”

After testing, EyeDAR was able to resolve target directions 200 times faster than conventional radar designs.

While EyeDAR currently targets risks associated with AVs, particularly in high-traffic urban areas, researchers also believe the technology behind it could complement artificial intelligence efforts and be integrated into robots, drones and wearable platforms.

“EyeDAR is an example of what I like to call ‘analog computing,’” Cho added in the release. “Over the past two decades, people have been focusing on the digital and software side of computation, and the analog, hardware side has been lagging behind. I want to explore this overlooked analog design space.”

12 winners named at CERAWeek clean tech pitch competition in Houston

top teams

Twelve teams from around the country, including several from Houston, took home top honors at this year's Energy Venture Day and Pitch Competition at CERAWeek.

The fast-paced event, held March 25, put on by Rice Alliance, Houston Energy Transition Initiative and TEX-E, invited 36 industry startups and five Texas-based student teams focused on driving efficiency and advancements in the energy transition to present 3.5-minute pitches before investors and industry partners during CERAWeek's Agora program.

The competition is a qualifying event for the Startup World Cup, where teams compete for a $1 million investment prize.

PolyJoule won in the Track C competition and was named the overall winner of the pitch event. The Boston-based company will go on to compete in the Startup World Cup held this fall in San Francisco.

PolyJoule was spun out of MIT and is developing conductive polymer battery technology for energy storage.

Rice University's Resonant Thermal Systems won the second-place prize and $15,000 in the student track, known as TEX-E. The team's STREED solution converts high-salinity water into fresh water while recovering valuable minerals.

Teams from the University of Texas won first and second place in the TEX-E competition, bringing home $25,000 and $10,000, respectively. The student winners were:

Companies that pitched in the three industry tracts competed for non-monetary awards. Here are the companies named "most-promising" by the judges:

Track A | Industrial Efficiency & Decarbonization

Track B | Advanced Manufacturing, Materials, & Other Advanced Technologies

  • First: Licube, based in Houston
  • Second: ZettaJoule, based in Houston and Maryland
  • Third: Oleo

Track C | Innovations for Traditional Energy, Electricity, & the Grid

The teams at this year's Energy Venture Day have collectively raised $707 million in funding, according to Rice. They represent six countries and 12 states. See the full list of companies and investor groups that participated here.

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This article originally appeared on our sister site, EnergyCapitalHTX.com.