Technology Applied in Bussiness

AI/ML
Radar

A magazine with a focus on technology advances adoption, value of data, and data literacy that are changing real competitive forces, and the risk associated.
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According to research firm Arize AI, the number of Fortune 500 companies citing AI as a risk in their annual financial reports hit 281 this year. That represents a 473.5% increase from 2022, when just 49 companies flagged the technology as a risk factor.
“The risks posed by AI systems are becoming increasingly significant as AI adoption accelerates across industry and society”

 

 

80% of Wall Street firms are studding and implementing AI Financial services executives report seeing an increase in quantifiable value from AI investments in the fifth annual Broadridge survey. 

Market data providers like Bloomberg and FactSet use generative AI to boost productivity for their users. Federal Reserve Gov. Michael Barr said the advent of generative artificial intelligence promises to boost bank productivity, but banks should be careful in choosing AI partners to delineate data security responsibilities. 

As AI becomes more pervasive, the tech industry has an important role to play in funding and scaling energy technologies as well as making chips, algorithms, and models more efficient, according to Deloitte. With Trump Tariffs, even AI could get more expensive. Higher prices may be in store for a range of materials used to build the data centers that will deliver AI.

The real risk of doing nothing

Many banks and credit unions hesitate to modernize their payment infrastructure, not because they don’t see the need – but because they don’t see immediate ROI. This inertia leads to shortsighted decisions, patchwork fixes, and mounting complexity that slows time to market and stifles innovation.

 The Competitive Gap: 60% of large businesses and 75% of SMBs are already shifting to non-bank payment providers—banks that don’t modernize risk losing relevance. 

The Budget vs. Execution Divide: Many banks allocate funds for modernization, but complexity, risk aversion, and lack of a clear roadmap slow execution.

The Business Case is Clear: Proven ROI includes increased straight-through processing (STP), improved customer experience, and faster time to market.

Meanwhile, forward-thinking banks are breaking free from legacy roadblocks, ensuring compliance readiness, delivering superior customer experiences, and adapting seamlessly to new payment channels. The key difference? They’ve invested in cleaning up their backend payment infrastructure – not just layering on cosmetic front-end fixes. By: Frank Gargano

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Strategic solutions fore many SB are linked to efficiency gains:

The rapid advancement of data access, AI is changing customer acquisition, payments, receivables collection and loyalty by improving 10 to 20% the return on investment (ROI).

Does AI increase cloud computing risks?

Citigroup has begun moving some software applications from its data centers to Google Cloud and is experimenting with Google’s AI technology through a multiyear agreement.

The bank plans to start with enterprise analytics, high performance computing for its markets business, desktop solutions for employees including customer service agents, and customer-facing apps. The move is part of a broader modernization effort that Citi CEO Jane Fraser mentioned in several earnings calls. It’s also part of an industry-wide shift toward cloud computing that has some regulators worried.

Citi is the latest large bank to announce a major move to cloud computing. In 2020, Capital One closed all its data centers and moved everything to Amazon Web Services. In 2021, JPMorgan Chase said it would use a cloud-based core banking system from Thought Machine for its retail bank, and Wells Fargo said it would migrate several applications to Microsoft Azure and Google Cloud. The next year, KeyBank said it would put primary applications in Google Cloud and U.S. Bank planned to move most applications to Microsoft Azure.

In a separate project, Citi’s developers continue to use GitHub Copilot to speed up their work.

Concerns?

The Treasury Department came out with a report last year that raised many concerns about the increasing use of cloud computing in financial services. Among them were issues around security, resilience, incident disclosure and response, concentration risk (too many banks relying on a small number of vendors, like Microsoft, Google, Amazon and IBM) and imbalances of power between bank users and large tech company providers.

Pro-Russia hackers target Italian banks in apparent DDoS attack.

A pro-Russia hacker group, NoName057, said it attacked the websites of various Italian institutions this week, starting with large banks. The hackers said they were reacting to a speech by Italian president Sergio Mattarella that compared Russia’s invasion of Ukraine to the „wars of conquest” by the Nazis.

Banks operating in the European Union will need to reach compliance next days with a major new law governing the stability of the financial system’s computer systems. Among other requirements, banks will be expected to monitor the risks presented by third-party technology vendors — a growing focus for U.S. regulators, as well.

Biden’s cybersecurity order could help banks deter fraud. President Joe Biden issued an executive order that could — if implemented by the incoming administration of Donald Trump — help banks and credit unions reduce fraud and financial crimes by improving the process for verifying government-issued identity information from customers and applicants.

Bad news for Cybersecurity institutions recruiting, talent shortage for Cybersecurity is growing. Job opportunities for cybersecurity specialists continue to grow significantly faster than other occupations, and cybersecurity experts are in high demand, especially in the financial services industry.

Management challenges

In „Republica,” Plato once noted that people, by nature, „will find no cessation from evil.” His observation is incredibly relevant today as our world faces major challenges like geo-political tensions (actual or hybrid), economic instabilities, demographic shifts, societal and political fractures, and widespread dissatisfaction and disengagement among various stakeholders. These forces, layered upon the aftermath of the worst pandemic in over a century, created a landscape where leaders have little time to absorb new concepts, leading to underperformance, stress, and burnout. A pressing question is how leaders can sustain their effectiveness and well-being in an environment defined by endless survival mode. (by Serban Toader)

Data in
credit Models

Traditional credit models tell only part of the story. In a market where accuracy, speed, and inclusion matter more than ever, lenders need deeper insights to make smarter decisions. 
 
Find the Right Customers. Keep the Right Customers. Reward the Right Customers.

Behavior scorecards can help lenders:

  • Unlock Hidden Opportunities: Use cash flow data to identify overlooked but creditworthy consumers.
  • Enhance Lending Decisions: Leverage updated data to improve speed and inclusivity.
  • Boost Customer Retention: Monitor financial health to proactively engage and support customers.
  • Drive Financial Inclusion: Tap into alternative data to serve the million considered credit underserved or unserved.
  • Build Smarter Strategies: Stay ahead in a shifting market with actionable insights across the full lending journey.
 

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AI/ML in Debt Collection

By combining traditional scorecards with ML models, financial institutions can create effective collections strategies tailored to each type of loan. This helps increase ROI based on improved recovery rates, and efficiency, ensuring better outcomes for both lenders and borrowers.

Using scorecards for customer behavior and other predictive models, whether traditional or based on machine learning (ML), can help improve collection rates and make the process more efficient.

  • Impact on Collections Efficiency using scorecard-based and ML-driven strategies:
  • Proactive Interventions: Data models help identify risks early, leading to quicker action. 
  • Better Borrower Segmentation: Scorecards provide initial grouping, and ML models adjust this over time, improving how lenders target borrowers. 
  • Personalized Recovery Plans: Both scorecards and ML models allow for repayment plans that are more tailored to each borrower, improving results. 
  • Higher Efficiency: Predictive models help prioritize cases, reducing costs and making collections more efficient.
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Case Example: Improving Collections for Unsecured Loans

A provider of unsecured loans used a mix of traditional scorecards and machine learning models, resulting in a 20% improvement in collections. The combination of early-stage borrower segmentation (using scorecards) and real-time analysis of borrower behavior (using ML models) helped target the right borrowers at the right time, improving recovery rates while cutting costs.

 

gen AI COST REDUCTION PATH

Generative AI offers potential for organizations seeking to optimize costs in today’s competitive landscape. This advanced technology can drive significant cost-reduction across an enterprise by automating repetitive tasks, generating insights from data, and identifying efficiency opportunities. A Generative AI model interprets data, recognizes patterns and relationships, and generates new texts, images, audio, videos, or code outputs using deep learning and neural networks. Rather than just analyzing information, they can create and innovate using human-like logic.
The potential for Generative AI is immense, but there are risks that accountants should be aware of when deciding to adopt Generative AI. Generative AI significant security risks that threaten data privacy, cybersecurity, and the integrity of digital systems. Risk around using gen AI can be classified based on two factors: intent and usage. Accidental misapplication of gen AI is different from deliberate. Is it important to understand business partners technology?

Lending Decisions

Traditional lending decisions depend on past performance, which does not always reflect a company’s ability to manage future growth. AI-driven credit models analyse:

01

Transaction activity

Revenue fluctuations, payment patterns, and cash flow.

02

Customer behaviour

Retention rates, purchase frequency, and engagement.

03

Market conditions

Industry trends, seasonal demand, and external economic factors.

Nonbank lenders have gained market share in the SBL market globally

Among nonbanks, fintech lenders , especially in USA, have become particularly active, leveraging alternative data and complex modeling for their own internal credit scoring.

According to a PwC report, Artificial Intelligence could contribute close to $15.7 trillion to the global economy and could be a massive opportunity.

“Small-business funding has become very data-heavy and data-driven, and there’s now much more cash-flow-based funding for small businesses than ever before.”

Fintech platforms’ internal credit scores were able to predict future loan performance more accurately than traditional credit scores, particularly in areas with high unemployment. Overall, while not all fintech firms follow the same approach, find that fintech lenders could help close the credit gap, allowing small businesses that were less likely to receive credit through traditional lenders to access credit and potentially at lower cost.
Small business lending (SBL) is on the cusp of funding priorities to handle higher levels of activity. SB alternative fintech, launch to offer solutions for underserved business, targeting critical survival aspects:
  • Credit access: The New York-based fintech’s BIZ2CREDT high-tech initiative is the centerpiece of an initiative aimed at capitalizing on an expected surge in small business lending activity.
  • Cash flow management: Quicken is launching a new money management product for small business owners that will compete with banks and pull in bank account data from aggregators
 

eU 2024 AI ACT
first AI low

Like the EU’s General Data Protection Regulation (GDPR) in 2018, the EU AI Act could become a global standard.  The following types of AI system are ‘Prohibited’ according to the AI Act.
  • deploying subliminal, manipulative, or deceptive techniques to distort behaviour and impair informed decision-making, causing significant harm.
  • exploiting vulnerabilities related to age, disability, or socio-economic circumstances to distort behaviour, causing significant harm.
  • biometric categorisation systems inferring sensitive attributes (race, political opinions, trade union membership, religious or philosophical beliefs, sex life, or sexual orientation), except labelling or filtering of lawfully acquired biometric datasets or when law enforcement categorises biometric data.
  • social scoring, i.e., evaluating or classifying individuals or groups based on social behaviour or personal traits, causing detrimental or unfavourable treatment of those people.
  • assessing the risk of an individual committing criminal offenses solely based on profiling or personality traits, except when used to augment human assessments based on objective, verifiable facts directly linked to criminal activity.
  • compiling facial recognition databases by untargeted scraping of facial images from the internet or CCTV footage.
  • inferring emotions in workplaces or educational institutions, except for medical or safety reasons.

Geopolitical Factor

In five of the last 10 transitions, a Democrat was succeeded by a Republican; each time the growth rate went down from one term to the next. In five of the transitions, a Republican was succeeded by a Democrat; each time the growth rate went up. No exceptions. Ten out of ten. What are the odds of this happening by chance? The answer is the same as the odds of getting heads on 10 coin tosses in a row: ½ times itself 10 times, which is 1 out of 1,024. In other words, the difference is statistically significant at the 99.9% level.

Academic point of view. Technology change vs “how institutions are formed".

Importance of societal institutions for achieving prosperity: 2024 ECONOMY NOBEL PRIZE. Vast differences in prosperity between nations: One important explanation is persistent differences in societal institutions. By examining the various political and economic systems, Daron Acemoglu, Simon Johnson and James A. Robinson have been able to demonstrate a relationship between institutions and prosperity. They have also developed theoretical tools that can explain why differences in institutions persist and how institutions can change.
The laureates’ theoretical framework for how political institutions are shaped and changed has three main components: A) a conflict between the elite and the masses; B) the masses are sometimes able to exercise power by mobilising and threatening the ruling elite; C) a commitment problem between the elite and the masses.
Our opinion is that institutions and capability to control new technology is at least the same important also  for new technology wave.

The Nobel Prize 2024​

“Reducing the vast differences in income between countries is one of our time’s greatest challenges. The laureates have demonstrated the importance of societal institutions for achieving this,” (Jakob Svensson)

Geopolitical tensions

Geopolitical tensions may spark from failure of economic policies, significant technology change, or geopolitical games, often linked with group interests divergent with country wealth, and also the weakened state power.

Historians view

US efforts to establish what resembles a hegemony in Europe and other parts of the world as a "77-year war" (from the WW1 to the fall of the USSR), has dominated the last phase of Western Civilization. (Prof Neagu Djuvara)

divide between academia & industry

At a global level, the growing divide between academia and industry means that research is increasingly concentrated where technology firms are most capable of developing advanced systems. In today’s economy, that’s the United States and China — the former benefiting in part from an influx of talent from other countries, the latter from the rapid rise of data-rich platforms such as WhatsApp — and, to a lesser extent, Canada. Europe, on the other hand, runs the risk of falling further behind.

Considering only information on the last 3 pages governments will soon be forced to do more than regulate. The role of government is not simply to regulate AI, for example Social media presents specific challenges, which often started as basic information feeds and developed into recommendation engines, forming and transforming peoples opinion, but mainly on mercantile interest or political, not real interested on their impact on young generations, or dependence vulnerability. (3 ways to center humans in artificial intelligence effortsFormer Google CEO Eric Schmidt on regulating AIEx-Google researcher: AI workers need protection).

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