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AI is Now Your Financial Guru

AI is Now Your Financial Guru

How well are investments understood today? If asked, many parents would likely suggest traditional investment options like a fixed deposit or gold. While these have been reliable in the past, they no longer meet the financial needs and aspirations of the current generation.

Data from the Reserve Bank of India (RBI) revealed that, as of December 31, 2020, two-thirds of Indian household financial assets were concentrated in bank deposits. Insurance funds, and provident and pension funds. This highlights a lack of awareness around widespread investment opportunities and a significant gap in investment diversification.

This gap in financial advisory services has paved the way for the rise of AI-driven solutions like MyFi. , co-founder and CEO of MyFi, India’s rapidly growing investor base, which includes over 100 million unique investors engaged in mutual funds, stocks, and other financial instruments. Has highlighted a critical shortage of expert financial advisors.

“India has only about 950 Registered Investment Advisors (RIAs), and even when mutual fund distributors are included, the total remains around 1,20,000 to 1,50,000. This disproportionate ratio of financial advisors to investors necessitates a more scalable solution, and that’s where AI steps in,” Nambiar explained.

MyFi, a SEBI-registered startup. Offers an AI-powered personal finance assistant that empowers individuals to make informed investment decisions. Through its integration with the RBI-approved account aggregator framework, MyFi enables individuals to log in with their PAN, phone number, and OTP, allowing them to access all their financial accounts seamlessly.

By combining this user data with daily market insights on mutual funds, stocks. And other financial instruments, MyFi delivers personalised recommendations and discovery functions. This democratisation of financial planning ensures that even retail investors can make data-driven investment choices without relying on traditional financial advisors.

AI isn’t just transforming personal finance. It’s reshaping the trading landscape as well. Noida-based uTrade Solutions is leading this revolution with its algorithmic trading platform. Specialising in trading solutions for stock brokers, fund managers, and retail investors, uTrade offers advanced automation tools that simplify trading strategies.

The organization’s no-code algorithmic trading platform allows investors, both experienced and beginners, to plan. Strategise, and automate their trades with minimal effort.

Seamless integration with insurance firm Share India allows traders to connect their Share India account instantly for a streamlined trading experience. Moreover, real-time AI-powered dashboards provide a comprehensive overview of portfolios, keeping traders ahead of market trends.

Despite AI’s growing influence, many traditional tax and. Accounting firms have been slow to adopt AI-driven solutions. ’s findings titled ‘2024 generative AI in professional services’, 30% of tax and accounting firms are still evaluating AI adoption. In contrast, 49% have no immediate plans to integrate AI into their workflows.

However, leading firms like Ernst & Young (EY), KPMG. And Deloitte have already embraced AI-powered automation. EY’s AI audit tools automate contract and document analysis with precision, drastically reducing manual effort. KPMG Ignite, an AI platform, enhances data-driven insights, offering predictive analytics and strategic financial guidance. Deloitte’s AI innovations streamline financial management with automation, minimising human errors and increasing efficiency.

Closer to India, Chennai-based startup Fhero Accounting Solutions is disrupting the industry by integrating AI into tax and. Accounting workflows. Fhero automates repetitive tasks such as data entry automation, capturing and categorising financial transactions from invoices, receipts. And bank statements with minimal manual input.

AI-driven analytics generate financial insights to assist clients in proactive decision-making. Furthermore, AI ensures error reduction, improving accuracy in record-keeping and compliance.

Today, how most people embrace LLMs and. Generative AI is still user-initiated to a large extent. The user asks a question, and it provides more information in response. It’s always the user taking the first step to engage.

Imagine a real-world scenario where a financial advisor isn’t simply reactive. Waiting for clients to ask for advice. Instead, they proactively offer guided advice based on financial and life circumstances. Whether it’s about marriage, having a child, starting a new job, or facing financial uncertainty, the advisor would take the clients’ unique circumstances into consideration.

“With AI today. We can automate a significant part of that process. While I’m not suggesting that recommendations should be entirely automated, AI can now understand a person’s financial landscape and. Deliver a highly personalised experience at scale. Right now, this level of service is typically reserved for high-net-worth individuals (HNIs) and ultra-HNIs with dedicated relationship managers. But why shouldn’t it be available to everyone? The technology exists, we just need to build it,” Nambiar added.

Maybe the only thing holding the industry back is the imagination in designing these experiences and. The time required to develop them.

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Progress Appoints Ed Keisling as Chief AI Officer to Drive AI Strategy

Progress Appoints Ed Keisling as Chief AI Officer to Drive AI Strategy

Progress, a global software enterprise, has appointed Ed Keisling as its Chief AI Officer (CAIO), a newly created role aimed at advancing the enterprise’s AI strategy and. Enhancing its product portfolio. Keisling will research directly to CEO Yogesh Gupta.

Keisling previously served as senior vice president of engineering for infrastructure management at Progress, where he played a key role in driving innovation and improving operational efficiency.

His extensive experience includes executive leadership roles in system architecture. Cloud computing, and infrastructure management. He was part of the leadership team at Vecna Technologies, overseeing engineering, IT, DevOps, support, program management, and analytics. He also spent over 17 years in senior engineering roles at Pegasystems.

Emphasising the significance of AI in redefining business operations, Keisling stated, “Our end-individuals rely on Progress to power and. Solve mission-critical parts of their business, and AI is redefining how these solutions evolve. With recent advancements in AI, the pace of innovation is accelerating, removing traditional barriers to adoption.”.

He added that Progress has been offering AI capabilities within its products for years and. Has passionate teams already focused on this space. And he is excited to further develop and lead this charge ensuring their consumers have the tools, processes, and expertise to fully leverage AI’s transformative potential and drive even further value with their products.

Beyond his corporate roles, Keisling is actively involved in mentoring and supporting engineering development programs, including the UNH Pathways Program and the MIT Undergraduate Practice Opportunities Program (UPOP). Where he serves as a mentor and presenter.

Gupta mentioned, “Ed is a transformational technology leader with over three decades of experience leading and driving change. Here at Progress, he has been instrumental in advancing our AI vision and innovation efforts. His ability to bridge engineering execution with strategic business goals has been evident throughout his career. Ed is shaping our AI-first approach as we align our product offerings to the rapidly evolving needs of our end-clients.”.

With a strong focus on AI-driven digital experience and infrastructure software. Progress continues to deliver AI-powered solutions to its global customer base.

As AI continues to be the centre of business strategy, the Chief AI Officer (CAIO) is now a new executive role becoming essential in corporate boardrooms. This position is emerging as a critical leader, shaping AI-driven transformation and steering companies toward a future where intelligent systems are integral to success.

’s research in 2024. Nearly 20% of organisations worldwide have designated a central team or leader to drive their AI strategy. Meanwhile, a LinkedIn findings revealed a skyrocketing demand for AI leadership, with “Head of AI” roles tripling in the past five years. Highlighting the growing recognition that AI needs dedicated oversight at the highest levels.

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Revolutionise Your Research with Axtria Rapid CSR for Faster, More Accurate Clinical Study Reports

Revolutionise Your Research with Axtria Rapid CSR for Faster, More Accurate Clinical Study Reports

Clinical study reports (CSRs) provide comprehensive summaries of clinical trials, detailing the critical elements of study design, endpoints, methodologies, results, and conclusions.

The creation of CSRs involves 16 distinct sections, requiring months of collaboration among various stakeholders who must analyze vast amounts of complex study data to develop customized summaries.

We can now do this superior, faster, and. More efficiently —we are announcing a bold, new breakthrough in the CSR authoring process: Axtria Rapid CSR.

This innovative solution is predicated on a multi-LLM approach capable of automating the creation of even the most comprehensive CSRs’ first draft.

The result? About 30-50% faster findings authoring, with 90% accuracy. And yes, it’s perfectly aligned with strict ICH-E3 standards. Axtria Rapid CSR has been extensively trained and tested and easily supports studies of all sizes, from early-phase trials to large-scale phase III trials and. Post-market surveillance.

The solution not only automates—it’s also smart. Axtria Rapid CSR draws critical inferences from multiple study data inputs, providing deeper context and relevance, even for the most complex sections of a findings. With it, we’ve seen significant advances in our ability to generate accurate medical writing, particularly in safety and. Efficacy.

This is a stroke of genius from our Clinical Solutions team. We believe CSR authoring will never be the same. Axtria Rapid CSR combines the power of artificial intelligence with human expertise to deliver superior results. It eliminates the tedious efforts of clinical researchers and medical writers, giving them the freedom to spend their time searching for revelations. Not repetition.

A 30-50% faster turnaround means substantial cost savings for life sciences companies. enhanced accuracy can mean quicker regulatory approvals. And that helps get the latest life-changing medications to the patients who need them most—at the right time.

Contact us at for a one-on-one demonstration and check out how Axtria Rapid CSR modernizes your clinical study study process, allowing for faster approvals. Cutting costs, and improving more lives.

Axtria works with 16 of the top 20 life science companies globally. In addition to Rapid CSR, Axtria’s years of domain experience in the industry guide pharma clients from brand launches to retirement. Their products go even further. Axtria InsightsMAx™ helps everyone from the C-suite to junior associates uncover trends to make superior decisions. Axtria SalesIQ™ helps optimise field forces and provider engagements.

Axtria CustomerIQ™ leverages AI-enabled next best action omnichannel choices. Axtria MarketingIQ™ turns investment analyses into pinpoint strategies. And Axtria DataMAx™ and DataMAx™ for Emerging Pharma is the data management framework that pulls it all together with best-in-class security and integration.

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Market Impact Analysis

Market Growth Trend

2018201920202021202220232024
23.1%27.8%29.2%32.4%34.2%35.2%35.6%
23.1%27.8%29.2%32.4%34.2%35.2%35.6% 2018201920202021202220232024

Quarterly Growth Rate

Q1 2024 Q2 2024 Q3 2024 Q4 2024
32.5% 34.8% 36.2% 35.6%
32.5% Q1 34.8% Q2 36.2% Q3 35.6% Q4

Market Segments and Growth Drivers

Segment Market Share Growth Rate
Machine Learning29%38.4%
Computer Vision18%35.7%
Natural Language Processing24%41.5%
Robotics15%22.3%
Other AI Technologies14%31.8%
Machine Learning29.0%Computer Vision18.0%Natural Language Processing24.0%Robotics15.0%Other AI Technologies14.0%

Technology Maturity Curve

Different technologies within the ecosystem are at varying stages of maturity:

Innovation Trigger Peak of Inflated Expectations Trough of Disillusionment Slope of Enlightenment Plateau of Productivity AI/ML Blockchain VR/AR Cloud Mobile

Competitive Landscape Analysis

Company Market Share
Google AI18.3%
Microsoft AI15.7%
IBM Watson11.2%
Amazon AI9.8%
OpenAI8.4%

Future Outlook and Predictions

The Your Financial Guru landscape is evolving rapidly, driven by technological advancements, changing threat vectors, and shifting business requirements. Based on current trends and expert analyses, we can anticipate several significant developments across different time horizons:

Year-by-Year Technology Evolution

Based on current trajectory and expert analyses, we can project the following development timeline:

2024Early adopters begin implementing specialized solutions with measurable results
2025Industry standards emerging to facilitate broader adoption and integration
2026Mainstream adoption begins as technical barriers are addressed
2027Integration with adjacent technologies creates new capabilities
2028Business models transform as capabilities mature
2029Technology becomes embedded in core infrastructure and processes
2030New paradigms emerge as the technology reaches full maturity

Technology Maturity Curve

Different technologies within the ecosystem are at varying stages of maturity, influencing adoption timelines and investment priorities:

Time / Development Stage Adoption / Maturity Innovation Early Adoption Growth Maturity Decline/Legacy Emerging Tech Current Focus Established Tech Mature Solutions (Interactive diagram available in full report)

Innovation Trigger

  • Generative AI for specialized domains
  • Blockchain for supply chain verification

Peak of Inflated Expectations

  • Digital twins for business processes
  • Quantum-resistant cryptography

Trough of Disillusionment

  • Consumer AR/VR applications
  • General-purpose blockchain

Slope of Enlightenment

  • AI-driven analytics
  • Edge computing

Plateau of Productivity

  • Cloud infrastructure
  • Mobile applications

Technology Evolution Timeline

1-2 Years
  • Improved generative models
  • specialized AI applications
3-5 Years
  • AI-human collaboration systems
  • multimodal AI platforms
5+ Years
  • General AI capabilities
  • AI-driven scientific breakthroughs

Expert Perspectives

Leading experts in the ai tech sector provide diverse perspectives on how the landscape will evolve over the coming years:

"The next frontier is AI systems that can reason across modalities and domains with minimal human guidance."

— AI Researcher

"Organizations that develop effective AI governance frameworks will gain competitive advantage."

— Industry Analyst

"The AI talent gap remains a critical barrier to implementation for most enterprises."

— Chief AI Officer

Areas of Expert Consensus

  • Acceleration of Innovation: The pace of technological evolution will continue to increase
  • Practical Integration: Focus will shift from proof-of-concept to operational deployment
  • Human-Technology Partnership: Most effective implementations will optimize human-machine collaboration
  • Regulatory Influence: Regulatory frameworks will increasingly shape technology development

Short-Term Outlook (1-2 Years)

In the immediate future, organizations will focus on implementing and optimizing currently available technologies to address pressing ai tech challenges:

  • Improved generative models
  • specialized AI applications
  • enhanced AI ethics frameworks

These developments will be characterized by incremental improvements to existing frameworks rather than revolutionary changes, with emphasis on practical deployment and measurable outcomes.

Mid-Term Outlook (3-5 Years)

As technologies mature and organizations adapt, more substantial transformations will emerge in how security is approached and implemented:

  • AI-human collaboration systems
  • multimodal AI platforms
  • democratized AI development

This period will see significant changes in security architecture and operational models, with increasing automation and integration between previously siloed security functions. Organizations will shift from reactive to proactive security postures.

Long-Term Outlook (5+ Years)

Looking further ahead, more fundamental shifts will reshape how cybersecurity is conceptualized and implemented across digital ecosystems:

  • General AI capabilities
  • AI-driven scientific breakthroughs
  • new computing paradigms

These long-term developments will likely require significant technical breakthroughs, new regulatory frameworks, and evolution in how organizations approach security as a fundamental business function rather than a technical discipline.

Key Risk Factors and Uncertainties

Several critical factors could significantly impact the trajectory of ai tech evolution:

Ethical concerns about AI decision-making
Data privacy regulations
Algorithm bias

Organizations should monitor these factors closely and develop contingency strategies to mitigate potential negative impacts on technology implementation timelines.

Alternative Future Scenarios

The evolution of technology can follow different paths depending on various factors including regulatory developments, investment trends, technological breakthroughs, and market adoption. We analyze three potential scenarios:

Optimistic Scenario

Responsible AI driving innovation while minimizing societal disruption

Key Drivers: Supportive regulatory environment, significant research breakthroughs, strong market incentives, and rapid user adoption.

Probability: 25-30%

Base Case Scenario

Incremental adoption with mixed societal impacts and ongoing ethical challenges

Key Drivers: Balanced regulatory approach, steady technological progress, and selective implementation based on clear ROI.

Probability: 50-60%

Conservative Scenario

Technical and ethical barriers creating significant implementation challenges

Key Drivers: Restrictive regulations, technical limitations, implementation challenges, and risk-averse organizational cultures.

Probability: 15-20%

Scenario Comparison Matrix

FactorOptimisticBase CaseConservative
Implementation TimelineAcceleratedSteadyDelayed
Market AdoptionWidespreadSelectiveLimited
Technology EvolutionRapidProgressiveIncremental
Regulatory EnvironmentSupportiveBalancedRestrictive
Business ImpactTransformativeSignificantModest

Transformational Impact

Redefinition of knowledge work, automation of creative processes. This evolution will necessitate significant changes in organizational structures, talent development, and strategic planning processes.

The convergence of multiple technological trends—including artificial intelligence, quantum computing, and ubiquitous connectivity—will create both unprecedented security challenges and innovative defensive capabilities.

Implementation Challenges

Ethical concerns, computing resource limitations, talent shortages. Organizations will need to develop comprehensive change management strategies to successfully navigate these transitions.

Regulatory uncertainty, particularly around emerging technologies like AI in security applications, will require flexible security architectures that can adapt to evolving compliance requirements.

Key Innovations to Watch

Multimodal learning, resource-efficient AI, transparent decision systems. Organizations should monitor these developments closely to maintain competitive advantages and effective security postures.

Strategic investments in research partnerships, technology pilots, and talent development will position forward-thinking organizations to leverage these innovations early in their development cycle.

Technical Glossary

Key technical terms and definitions to help understand the technologies discussed in this article.

Understanding the following technical concepts is essential for grasping the full implications of the security threats and defensive measures discussed in this article. These definitions provide context for both technical and non-technical readers.

Filter by difficulty:

DevOps intermediate

algorithm

generative AI intermediate

interface

algorithm intermediate

platform

API beginner

encryption APIs serve as the connective tissue in modern software architectures, enabling different applications and services to communicate and share data according to defined protocols and data formats.
API concept visualizationHow APIs enable communication between different software systems
Example: Cloud service providers like AWS, Google Cloud, and Azure offer extensive APIs that allow organizations to programmatically provision and manage infrastructure and services.

cloud computing intermediate

API

platform intermediate

cloud computing Platforms provide standardized environments that reduce development complexity and enable ecosystem growth through shared functionality and integration capabilities.

large language model intermediate

middleware