Tag: Natural Language Processing

Generative AI

Transforming M&A: The Role of Generative AI

Nehatrendy

Generative AI in M&A Deals

About one in five surveyed companies currently uses Generative AI in Mergers and Acquisitions (M&A) processes, and over half expect to integrate it into their dealmaking soon. Early adopters report that expanding generative AI tools across more M&A stages multiplies the benefits. Traditionally, M&As are strategies companies use for growth. Merging with another company in the same sector can boost market share and revenue, while acquisitions can involve purchasing a business in a different industry.

Generative AI is poised to become a permanent fixture in M&A. Its potential is vast, with a profound impact expected on dealmaking. Companies that master generative AI in M&A over the next five years are likely to identify targets faster, underwrite deal value with increased confidence, conduct diligence and integration more efficiently, and ultimately achieve higher shareholder returns.

Notably, in the latest survey, 36% of the most active acquirers use generative AI for M&A. This matters because long-term M&A studies show that companies making regular deals consistently outperform less active firms in total shareholder returns. Private equity is also an enthusiastic early adopter, with over 60% of surveyed firms leveraging at least one generative AI tool to improve sourcing, screening, or diligence.

Beyond accelerating sourcing, screening, and diligence, early adopters are now experimenting with generative AI for integration, divestiture planning, and program management.

How Generative AI is gaining traction in M&A deals

According to recent surveys, nearly 80% of companies leveraging Generative AI in their M&A processes report reduced manual effort. As a result, employees spend less time on the M&A program and can more easily balance demands from the core business. Key business cases for incorporating Gen AI into M&A deals include.

Advancing the Screening / Sourcing Process

While traditional AI has already proven effective in deal scanning, Gen AI enhances this by analyzing broader sets of both structured and unstructured data, synthesizing results for quantitative and qualitative insights, and highlighting key elements such as strategic, financial, and cultural fit of potential targets. This enables companies to identify and pursue targets that might otherwise go unnoticed.

Expediting Diligence and Negotiations

Generative AI accelerates the diligence and negotiation stages by summarising key documents, surfacing risks, drafting initial memoranda tailored to deal with specifics, sourcing relevant statutes and regulations, and pinpointing useful case law to ease negotiation friction. It can also generate highly accurate first drafts for deal announcements and regulatory filings. Collectively, these capabilities can significantly reduce the time required for various legal tasks associated with deal negotiations, signing, and closing.

Executing Integration or Separation Effectively

Experienced dealmakers understand the importance of quickly capturing deal synergies; delays can lead to missed value. Organizations engaged in significant M&A events are particularly prone to distractions that may result in a decline in organic momentum and an average decrease in excess revenue growth compared to peers.

Enabling Generative AI in M&A deals

74% percent of C-suite executives anticipate increasing their investments in data and generative AI technologies over the next decade, aiming to drive business transformation.

Task Standardization

Standardised tasks that are ideal for automation include designing Transition Service Agreements (TSAs) and integrating systems and infrastructures. Creative tasks benefiting from data augmentation include designing operating models, producing deal communications, and facilitating post-merger performance assessments.

Early adopters are using generative AI to draft integration workplans and TSAs in less than 20% of the time previously required, enabling faster team mobilization with better quality information

Generative AI can enhance internal capabilities by leveraging proprietary data from prior deals to assess performance patterns and identify new opportunities. For instance, AI can evaluate an acquisition portfolio and measure the impact of individual deals.

AI can efficiently analyse large volumes of data, such as reviewing material contracts and identifying deviations from model contracts, saving time and focusing attention on critical issues.

Value Creation

Generative AI tools allow access to specific company data to size realistic cost and revenue synergies and design value creation plans informed by historical acquisition performance.

A key use case is a generative AI “coach” trained on M&A best practices and the organisation’s unique playbook, providing rapid, insightful answers to integration and separation teams

Companies using generative AI can improve due diligence and value realization by identifying cost and revenue synergies and refining plans to achieve them. Tailored AI tools can leverage sales, pricing, customer relations, and catalog data to prioritize cross-selling targets and accelerate post-close value capture.

Leading research organizations recommend applying AI within internal M&A processes first by developing and testing diverse use cases. In particular, contract analytics is an impactful application for using AI right now to improve activities tied to letter of intent (LOI) negotiation, contract due diligence, definitive agreement and TSA negotiation, and contract renewals and integration.

Generative AI reduces manual efforts in M&A, allowing employees to focus on core business priorities. Nearly 80% of businesses report reduced manual work and improved efficiency and retention when using AI in their M&A processes.

Productivity and Efficiency

Generative AI-powered tools automate and accelerate a wide array of time- and resource-intensive tasks. Innovations like virtual data rooms, cloud-based integration tools, and advanced analytics have transformed due diligence, collaboration, risk assessment, and decision-making, resulting in smoother business integrations and improved deal outcomes.

One user described utilizing third-party tools for managing data rooms, including automated filing, advanced search, and streamlined Q&A processes.

Among surveyed companies, 78% report productivity gains from reduced manual effort, 54% saw faster timelines, 42% realized cost savings and better focus, and 85% of early users stated that AI met or exceeded expectations

Together, these post-deal applications of generative AI represent a significant and relatively untapped opportunity for dealmakers aiming to comprehensively reinvent their companies’ M&A functions. By developing and implementing a formal strategy, executives can simultaneously address related priorities, such as talent, technology maturity, and business value validation.

Adoption of Generative AI in M&A deals

Adoption of generative AI in M&A deal processes is currently low at 16%, but this is projected to rise to 80% over the next three years. Notably, 85% of current users report that generative AI meets or exceeds their expectations. Senior leaders should begin by honestly assessing their current M&A capabilities and identifying where technology can meaningfully improve their M&A processes.

Start Adopting Now

New technologies require testing, learning, and time to build expertise, identify valuable use cases, and drive user buy-in. Companies can’t simply purchase a generative AI–enabled solution later and expect to catch up with early adopters, as the learning curve will be steeper. While mastery will take time, businesses can start reducing manual process time within the first week of implementation. Prioritize generative AI use cases with the highest impact. For M&A strategies targeting many small acquisitions, generative AI will add the most value in opportunity scanning and assessment.

Build an AI Portfolio

A basic generative AI tool might be as simple as a well-designed prompt drawing upon high-quality data sources. At a minimum, companies can make substantial progress by securing an enterprise license and using these tools. When bringing prioritized use cases to life, carefully evaluate whether to build in-house or adopt off-the-shelf solutions. Recently, more turnkey solutions have entered the market and additional options are expected in the next one to two years.

Innovate with Purpose

Leading companies will move beyond simple automation, reimagining their end-to-end M&A processes to fully leverage generative AI. They’ll strategically invest in areas offering competitive advantages. As with any build-or-buy decision, leaders should weigh their team’s expertise, investment size, potential returns, and actions of peers.

Evolve the M&A Team

Generative AI is set to assume more of the time-intensive project management. This prompts a reevaluation of future skill needs for M&A. The most successful deals are delivered by professionals focused on strategic value rather than process management. To stay ahead, companies should revisit their talent strategies, aiming to build and sustain long-term value through generative AI.

Managing Risks in M&A deals

Mergers and acquisitions (M&A) can offer substantial benefits to both parties involved, but they may also present challenges, particularly when regulatory issues or cultural clashes arise. As more companies leverage advancements like generative AI, late adopters may struggle in three key areas:

Ensuring proper guardrails are in place. Generative AI differs from many existing technologies in that it amplifies certain risks, such as increased potential for security breaches due to its accessibility, reputational risks from quality control failures, and intellectual property infringement. Legal and regulatory environments are evolving rapidly, even as generative AI progresses.

Increased reliance on advanced AI models can raise the risk of human disengagement, potentially allowing critical issues to go unnoticed. It’s crucial for organizations to keep humans involved, work proactively with legal and technology teams to identify and mitigate risks, and uphold strong ethical standards.

Generative AI can drive significant process efficiencies in M&A, but it also brings risks such as data inaccuracies, privacy concerns, and cybersecurity threats

Generative AI accelerates data summarisation, reducing the time from a week to a day and enabling teams to focus on deeper analysis. This allows firms to make better-informed bids and quickly identify promising opportunities—or know when to walk away.

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Artificial Intelligence, Generative AI, Industry Perspectives, Technology & Digital

Understanding Agentic AI: The Future of Autonomous Customer Service

Nehatrendy
Generated by Gen AI

What is Agentic AI?

Gartner1 predicts Agentic AI will autonomously resolve 80% of common customer service issues without human Intervention by 2029. Despite AI’s rise, 89% of customers emphasize the need to combine human connection with AI efficiency to optimize experiences.

Agentic AI, also known as autonomous AI, is a form of artificial intelligence that operates independently to design, execute, and optimize workflows. This empowers enterprises to make decisions and complete tasks more effectively. These AI agents can plan, adapt, and achieve predefined goals without human intervention.

Agentic AI leverages advanced reasoning and iterative planning to solve complex, multi-step problems autonomously. In contrast, AI chatbots typically use Generative AI to respond to single queries, processing one interaction at a time using Natural Language Processing (NLP).

The next frontier in AI is Agentic AI, poised to enhance productivity and operational efficiency across industries. These systems can process vast amounts of data from various sources and third-party applications, independently analyzing challenges, developing strategies, and executing tasks. Businesses are adopting Agentic AI to personalize customer service, streamline software development, and facilitate patient interactions.

For example, an AI agent in customer service could go beyond answering questions. It could check a user’s outstanding balance, recommend ways to pay it off, and complete the transaction after user approval.

How does Agentic AI work?

Define your objectives, and Agentic AI takes action. If a suitable workflow exists, the agent executes it; otherwise, agents may design their own workflows on the fly, always consulting with humans when needed. If circumstances change, AI agents adapt their strategies to achieve optimal results. Agentic AI proactively anticipates needs, predicts outcomes, and responds to opportunities.

Agentic AI follows a four-step process for problem-solving:

  • Perceive: Agents gather and process data from various sources—sensors, databases, and digital interfaces—extracting features, recognizing objects, or identifying relevant entities in the environment
  • Reason: A large language model (LLM) orchestrates tasks, generates solutions, and coordinates specialized models for functions like content creation, visual processing, or recommendations. Techniques such as retrieval-augmented generation (RAG) enable access to proprietary data for accurate, relevant outputs
  • Act: Agentic AI swiftly executes its plans by connecting with external tools and software via APIs. Built-in guardrails ensure tasks are performed correctly; for instance, a customer service AI may handle claims up to a set amount, with larger claims requiring human approval
  • Learn: Agentic AI continuously improves through a feedback loop (data flywheel), feeding interaction-generated data into the system to enhance models. This adaptive capability helps businesses drive better decision-making and operational efficiency

Capabilities of Agentic AI

  • Workflow Design: Use AI agents to implement best practices and rapidly create new workflows
  • AI-Powered Decisioning: Automate decision-making with context-rich data to determine the next best action or enable straight-through processing
  • Adaptability: Enhance business agility as Agentic AI adapts to change and predicts future scenarios, ensuring optimal outcomes
  • Workflow Execution: AI agents identify and execute the appropriate workflows to resolve user requests automatically
  • Workflow Optimization: Leverage AI agents to optimize and execute complex, multi-step workflows

Spot the Difference: Generative AI Vs. Agentic AI

Artificial intelligence (AI) has become a buzzword in today’s world. The term often brings to mind robots and self-learning machines, but in reality, AI encompasses a wide range of subfields. Two of the most discussed advancements are Generative AI and Agentic AI. It is crucial to understand that they operate in fundamentally different ways. Grasping these distinctions is essential for understanding how AI is shaping and will continue to shape our world.

Generative AI

Generative AI is focused on creation—think of it as the inventive branch of artificial intelligence. These systems are designed to produce a wide range of content, including text, images, music, code, and even videos. By learning from existing data, generative AI generates original outputs that emulate human creativity. The advent of tools like ChatGPT, DALL·E, and MidJourney has brought Generative AI into the mainstream. These platforms rely on advanced machine learning models, especially neural networks, to analyse patterns from their training data and replicate them.

However, Generative AI is not without flaws. Its outputs are constrained by the quality of its training data: if the data is biased or incomplete, the results will reflect those limitations. Moreover, Generative AI does not truly “understand” the content it creates; it predicts subsequent elements based on recognized patterns. Despite these drawbacks, Generative AI is rapidly transforming industries ranging from marketing to entertainment.

Agentic AI

Agentic AI systems do more than generate outputs—they make decisions, take action, and adapt to changing environments. Operating as intelligent agents, they make real-time decisions, dynamically adapt, and integrate into various systems or workflows. Agentic AI powers technologies such as autonomous vehicles, robotic process automation, and smart assistants capable of scheduling meetings or ordering groceries. These systems use sensors, algorithms, and actuators to perceive their environment, process information, and execute actions.

What sets Agentic AI apart is its purposeful decision-making. Instead of merely responding to inputs, it acts based on objectives, choosing strategies to achieve its goals. For instance, an autonomous drone making deliveries must avoid obstacles, optimize its route, and adapt to unexpected events—all without human intervention.

However, granting these systems autonomy raises important ethical and accountability concerns. Who is responsible if an autonomous system makes an error? How do we ensure such systems align with human values? Addressing these challenges is crucial as Agentic AI becomes more widespread.

Summarizing the Core Differences

The simplest way to distinguish Generative AI from Agentic AI is to consider their main functions. Generative AI focuses on creating new content, whereas Agentic AI is designed to accomplish specific goals. In essence, one generates, while the other acts.

Generative AI is generally static; it produces outputs based on previously learned data but does not interact with or adapt to its environment in real time. Its activities are confined within preset boundaries. Agentic AI, by contrast, is dynamic: it continually processes new information, learns from its surroundings, and adjusts its actions to achieve objectives.

Another important difference is in the complexity of their tasks. Generative AI is best suited for narrow, well-defined problems—such as writing a paragraph or creating digital art. Agentic AI typically handles more complex, multi-step tasks that require ongoing decision-making and adaptation.

Benefits of Agentic AI

  • Increased efficiency: By automating workflows and enabling real-time decision-making, AI agents support straight-through processing, transforming your enterprise into an operations powerhouse
  • Enhanced self-service: When interacting with a chatbot, Agentic AI understands the customer’s intent and guides them to resolve issues or workflows on their own, without needing escalation to a live agent
  • Maximized employee performance: AI agents can assess complex situations and provide employees with step-by-step guidance based on your organization’s policies and best practices
  • Better and faster workflow design: Agentic AI, such as Pega Blueprint™, recommends workflow designs that follow industry best practices—accelerating speed to market and serving as a creative partner for developers
  • Hyper-personalized engagement: By analyzing vast data, AI agents understand customer needs and determine the best course of action in real time, delivering highly personalized experiences

Challenges and Considerations of Agentic AI

  • Cost of Implementation: Integrating Agentic AI can be expensive, especially for mid-sized businesses. Costs may include upgrading infrastructure, licensing software, and providing training for staff. Careful planning and phased implementation can help manage these expenses. And over time, the long-term ROI from time and efficiency savings may balance out the upfront costs. 
  • Implementation Barriers: Adopting Agentic AI requires investment in technology and integration. You may need to upgrade legacy systems and provide training to ensure agents can use the tools effectively
  • Training and Adoption: Agents might initially struggle with adapting to this new way of working. Proper onboarding and ongoing support are vital to building trust in the technology and maximizing its benefits
  • Ethical and Transparency Concerns: These are a major issue in AI deployment. People are especially worried about bias in AI models, and their personal privacy. So, ensure you are transparent with your customers when AI is used
  • Balancing AI and Human Touch: While Agentic AI does improve efficiency, we need to keep the human touch. This builds trust and rapport. Striking the right balance ensures customers feel valued and heard, not just processed

What are some Use Cases for Agentic AI?

Customer Service

By 2028, 68% of all customer service and support interactions with technology vendors are expected to be handled by Agentic AI

In self-service situations, AI agents can understand customer intent and determine the right workflow to guide customers in resolving their inquiries. Agentic AI can also assist customer service reps with their work by suggesting actions and automating tasks and processes. AI agents are improving customer support by enhancing self-service capabilities and automating routine communications. Over half of service professionals report significant improvements in customer interactions, reducing response times and boosting satisfaction. There’s also growing interest in digital humans — AI-powered agents that embody a company’s brand and offer lifelike, real-time interactions to help sales representatives answer customer queries or solve issues directly when call volumes are high.

Content Creation

Agentic AI enables rapid creation of high-quality, personalized marketing content. Generative AI agents save marketers an average of three hours per content piece, freeing them to focus on strategy and innovation. Streamlining content creation helps businesses remain competitive and boost customer engagement.

On the performance side, AI agents can scan hundreds of millions of pages, analyze metrics, flag optimization issues and fine-tune content. Previously, this could take months or even years. Teams no longer need to wait for underperformance to show up in a dashboard—now they can identify issues before taking a hit on traffic or conversions.

Healthcare

Key applications of Agentic AI in healthcare include:

  • Workflow automation: Agentic AI automates tasks like staff scheduling, patient intake, documentation, and many other routine manual tasks to free up clinical teams to focus on patient care.
  • Real-time decision-making support: AI Agents can support clinicians by providing data-informed choices in the moment and taking actions without human guidance. AI agents can also provide 24/7 support, offering information about prescribed medication usage, appointment scheduling and reminders, and additional resources to help patients adhere to treatment plans.
  • Adaptive learning: Agentic systems can adapt to changing circumstances and continuously improve their performance by interacting with APIs, digital tools, and evolving data sets in real-world healthcare environments. Agentic AI brings significant value to healthcare by analyzing patient health data for proactive care, scheduling appointments, and guiding both patients and providers through the complexities of healthcare journeys.

Software Engineering

AI agents are enhancing developer productivity by automating repetitive coding tasks. Projections suggest that by 2030, AI could automate up to 30% of work hours, enabling developers to focus on more complex challenges and drive innovation. With growing IT complexity, 92% of organizations say the support and services they receive are more important than ever.

Video Analytics

Enterprises and public sector organizations around the world are developing Video Analytics AI Agent to boost the capabilities of workforce that rely on visual information from a growing number of devices — including cameras, IoT sensors and vehicles. Video analytics AI agents can analyze large amounts of live or archived videos, request tasks via natural language and perform complex operations like video search, summarization and visual question-answering. These agents can also be used to deliver anomaly alerts, draft incident reports, improve quality control through visual inspection and enhance predictive maintenance. 

Unlike conventional rule-based systems, Agentic AI video analytics adapts dynamically to context, learns from ongoing interactions, and collaborates across enterprise systems. The ability to process video streams in real time with semantic accuracy not only enhances situational awareness but also improves predictive capabilities—driving faster, more informed business outcomes.

Financial Services

Imagine a trading AI agent that analyzes market data and autonomously monitors market trends, deciphers trading signals, adjusts strategies and mitigates risks in real time. Agentic AI will make this increasingly possible, integrating tools via APIs, sensors and advanced reasoning, to respond to new information and data, and automate and enhance financial tasks beyond Gen AI capabilities. Agentic AI’s increased autonomy, relative to Gen AI, enables it to handle repetitive, data-intensive processes. This gives financial institutions and market participants an increased capability to optimize workflows, enhance compliance and improve decision-making, transforming how financial services conduct business and interact with customers in the age of Agentic AI.

Conclusion: The Future of Agentic AI

Agentic AI holds tremendous potential to transform enterprises. Because it operates autonomously, companies will need frameworks that support autonomous workflows, provide seamless data integration, and continually optimize processes. This new technology will drive enterprises to undergo broader transformations to fully harness Agentic AI’s capabilities.

Agentic AI is still in its early stages. However, it has the potential to really transform customer service and other aligned avenues. Here’s a glimpse into what the future might hold:

  • Hyper-Personalization: As Agentic AI matures, its ability to analyze and predict customer behaviour might improve, though accuracy may vary depending on the data and context. This may lead to hyper-personalized customer experiences, where interactions are tailored not just to past behaviour but to real-time needs and preferences
  • Enhanced Multi-Channel Support: Agentic AI appears ready to make omnichannel customer service truly consistent. Whether a customer reaches out via messaging, chat, email, voice, or video, the AI could provide consistent, informed support and ensure smooth transitions between channels

The distinction between Generative and Agentic AI will blur as both evolve. Advancements may yield systems that merge creation and action, offering the best of both worlds. Imagine an AI that not only generates innovative ideas but also implements them autonomously—a true game-changer across industries like healthcare and manufacturing.

However, this potential comes with responsibility. These technologies must be developed and deployed ethically, with transparency and accountability. Understanding the differences between Generative and Agentic AI will help us appreciate their strengths and guide us toward a future where AI meaningfully benefits humanity.

References

  1. https://www.gartner.com/en/newsroom/press-releases/2025-03-05-gartner-predicts-agentic-ai-will-autonomously-resolve-80-percent-of-common-customer-service-issues-without-human-intervention-by-20290 ↩︎
Artificial Intelligence, Generative AI, Industry Perspectives, Technology & Digital

AI Innovations Transforming Financial Services

Nehatrendy

A Primer to Artificial Intelligence in Finance

Artificial Intelligence (AI) in finance refers to fintech innovations that enable financial services organizations to enhance the efficiency, accuracy, and speed of tasks such as data analytics, forecasting, investment management, risk management, fraud detection, and customer service. AI is modernizing the financial industry by automating traditionally manual banking processes, improving the understanding of financial markets, and creating customer engagement methods that emulate human intelligence and interaction.

AI models execute trades with unparalleled speed and precision, leveraging real-time market data to uncover deeper insights and inform investment decisions. By analyzing complex patterns in transaction data, AI solutions help financial organizations enhance their risk management efforts, including security, fraud prevention, anti-money laundering (AML), know your customer (KYC) compliance, and other regulatory initiatives. 

Furthermore, AI is changing how financial organizations interact with customers by predicting their behaviors and understanding their purchasing preferences. This leads to more personalized interactions, faster and more accurate customer support, improved credit scoring, and the development of innovative banking products and services.

In summary, Artificial Intelligence is a crucial tool for the banking and finance industry, allowing financial institutions to process large volumes of data more quickly and accurately. This capability to analyze extensive data in real time is essential for identifying patterns and trends, detecting fraud and financial crimes, and making reliable predictions.

Benefits of AI in Finance

AI technology has the potential to significantly enhance accuracy and precision in decision-making and risk management, particularly within the financial industry. One of the main advantages of using AI in this context is its ability to make unbiased decisions, leading to quicker and more precise loan eligibility determinations.

Enhancing Customer Experience

One of the most noticeable impacts of AI in financial services is the transformation of customer experience. Chatbots and virtual assistants powered by large language models provide customers with 24/7 support, addressing queries and solving problems in real time. AI-driven chatbots can manage a wide range of customer interactions, from answering basic questions about account balances to providing detailed information about complex financial products. This level of service availability ensures customers receive timely assistance, improving their overall experience and fostering loyalty. Additionally, AI tools can handle multiple interactions simultaneously, significantly increasing efficiency compared to human-operated customer service centers. Personalized banking experiences are another important benefit; AI algorithms analyze vast amounts of customer data to offer tailored financial advice, product recommendations, and investment strategies.

Strengthening Security and Fraud Detection

Many financial institutions are leveraging AI for fraud detection by using sophisticated algorithms that identify unusual patterns in customer behavior. These models continuously learn from the large datasets generated by users, enhancing their ability to flag fraudulent activities with high accuracy—something humans could not achieve at scale. AI systems can flag suspicious transactions and interactions in real time, allowing institutions to act swiftly and prevent potential losses. 

Data-Driven Decision Making

Historically, the financial sector has relied heavily on human expertise for decision-making processes. However, the enormous volume of data generated in today’s digital age necessitates more advanced analytical tools. AI, with its ability to process and analyze massive datasets at incredible speeds, provides a solution that is both efficient and highly accurate. By leveraging AI, financial institutions can uncover valuable insights from their data, enabling them to forecast market trends, customer behaviors, and potential risks with unprecedented accuracy.

Streamline Operations

Many back-office functions in financial institutions, such as data entry, reconciliation, report generation, and data backups, can be automated using AI. Automating these mundane and repetitive tasks not only enhances efficiency but also reduces the likelihood of human error. This allows employees to focus on product development and strategic activities. Financial institutions that effectively implement automation can operate with leaner teams, thereby reducing overhead expenses. 

Industry Applications of AI in Finance

Consumer Finance

Artificial Intelligence (AI) is playing a crucial role in preventing fraud and cyberattacks in the finance industry, addressing a major concern for consumers who seek secure accounts. For instance, JPMorgan Chase has adopted AI to enhance the security of its account holders by using a proprietary algorithm that detects fraud patterns. When a credit card transaction is processed, the transaction details are sent to central computers that assess whether the transaction is fraudulent.

Personal Finance

AI is gaining traction as consumers desire more control over their financial health. To remain competitive, financial institutions are implementing AI technologies that provide services like personalized wealth management and 24/7 financial guidance through chatbots powered by natural language processing. For example, Eno utilizes over 12 proactive capabilities to generate insights and anticipate customer needs, such as alerting customers to suspected fraud or notifying them of price increases in subscription services.

Corporate Finance

In the realm of corporate finance, AI proves to be valuable for predicting and assessing loan risks, reducing financial crimes, and enhancing loan underwriting processes. AI can analyze data to identify anomalies that may be missed by human analysts, thereby assisting companies in making informed decisions and mitigating financial risks. For instance, banks are leveraging deep learning techniques to analyze customer data and detect potential fraudulent activities like money laundering. 

FinTech

Banks are under pressure to enhance their digital services due to the growing demand from consumers and competition from tech-savvy startups. To cater to the needs of younger customers, banks are boosting their IT and AI budgets. Notably, Millennials and Gen Z prefer digital banking channels over visiting physical branches, with 78% choosing to avoid branch visits altogether.

Role of Intelligent Finance

Trading and Investment Management

  • Investment Software for Recommendations: Banks constantly seek profitable investment opportunities beyond just earning interest income. They utilize investment software to provide recommendations that align with their risk appetite. This enables them to make informed investment decisions and achieve healthy returns.
  • Accurate Evaluation of Client Proposals: Understanding industry-specific information can be challenging, but investment software can accurately evaluate client funding proposals. It takes into account various factors and accommodates more variables, simplifying the process for human analysts.
  • Accelerating the Assessment of New Environments: When institutions consider investing outside their national borders, accessing relevant information can be time-consuming. However, advanced AI software can expedite this process by quickly assessing new environments and providing valuable insights for investment decisions.
  • Algorithmic Trading: AI can be utilized to develop trading algorithms that analyze market trends and historical data, allowing for faster decision-making and execution of trades compared to human traders.

Risk Management and Fraud Detection

  • Decision Management System and Audit Documentation: Implementing a Decision Management System can facilitate early detection of fraudulent activities and improve audit documentation. Third-party audits can disrupt regular operations, especially when employees need to provide missing information.
  • Importance of Artificial Intelligence in Fraud Detection: As financial institutions enhance their vigilance against fraudulent activities, fraudsters are adapting their tactics. They have learned to conduct transactions just below detection thresholds to avoid triggering investigations. Without proper analysis, criminal activities may go undetected, even when they comply with prescribed regulations. This is where artificial intelligence (AI) excels, by processing vast amounts of data, AI can identify suspicious transactions more effectively, whereas manual analysis is often prone to error.
  • Risk Management: AI can analyze data to assist financial organizations in assessing and managing risks more effectively, creating a more secure and stable financial environment.
  • Fraud Detection: AI algorithms play a crucial role in preventing financial crimes, such as fraud and cyberattacks, by identifying unusual patterns in financial transactions. This capability enhances security in activities like online banking and credit card transactions.

Future of AI in Financial Services

AI will play a crucial role in driving growth in the financial services sector. Many organizations have transitioned to digital platforms, discovering new ways to sell, improve efficiencies, and utilize their data effectively. Moving forward, it will be essential for these organizations to personalize customer engagement on a large scale. AI is instrumental in delivering tailored customer responses, making safer and more accountable product and service recommendations.

Additionally, financial institutions will need to create strong and unique permission-based digital customer profiles. However, the necessary data may often be trapped in silos. By breaking down these silos, implementing an AI layer, and incorporating human engagement seamlessly, financial institutions can develop experiences that cater to the unique needs of their customers while operating efficiently.

Generative AI in Finance

Finance teams are exploring how generative AI can enhance existing processes by creating text and conducting research. In the future, integrating generative AI will transform essential processes, redefine business partnerships, and help mitigate risks. Generative AI will eventually work alongside traditional AI forecasting tools to generate reports, explain variances, and provide recommendations. These improvements will empower finance professionals to make more informed strategic decisions, ultimately boosting operational efficiency and effectiveness.

  • Anomaly Detection: AI can detect anomalies such as fraudulent transactions, financial crimes, spoofing in trading, and cyber threats.
  • Sentiment Analysis: AI can identify sentiment in various texts to gauge emotional opinions on topics like investment research and chat data
  • Market Sentiment Analysis: AI can analyze news sources, social media, and other information to assess market sentiment, which helps predict market trends and influences decision-making
  • Document Processing: AI can extract structured and unstructured data from documents and analyze, search, and store this data for document-intensive processes such as loan servicing and investment opportunity discovery.
  • Data Science and Analytics: Organizations can access a comprehensive suite of data management, analytics, and machine learning tools to generate insights and unlock value from data for business intelligence and decision-making.
  • Predictive Modeling: AI can utilize customer, risk, transaction, trading, and other data insights to predict specific future outcomes with a high degree of precision. These capabilities are beneficial for fraud detection, risk reduction, and anticipating customer needs.
  • Predictive Analytics: AI enables predictive modeling, helping financial organizations forecast market trends, potential risks, and customer behavior.

For the finance and controllership workforce, finance leaders and accounting professionals can also use Generative AI tools targeted specifically for their role in the function or professional motivations.

  • Finance leaders can use Generative AI to maintain a pulse on the business and adapt to market conditions, predict and preempt strategic macroeconomic blockers, enhance organizational structure, and provide quick answers to evolving questions and real-time information.
  • Directors and managers that need to focus time on strategic process improvements; improve budget efficiencies; provide key, timely insights to enable business decisions; synthesize information to understand problems; and streamline processes can use Generative AI to conduct trend analysis, proactively manage organizational spending, generate insights from emails and reports, and perform tasks that can help drive management efficiencies.
  • Experts can use Generative AI to run intelligent searches of knowledge bases, standard operational procedures, and regulatory documents; generate control compliance reports to provide domain-specific expertise to business decisions; and monitor compliance, ethics, and control across the business.
  • Finance and accounting analysts can use Generative AI tools like virtual assistants and intelligent bots to deliver vital operational tasks faster and more efficiently and respond to ad hoc reporting requests. AI applications such as virtual finance analysts can also help analysts drive operational excellence and value-added strategic tasks.

Wrapping Up

In today’s fast-paced world, artificial intelligence (AI) has emerged as a key driver of innovation in finance. Its capabilities include enhancing customer experience, detecting fraudulent transactions, assessing risks, and developing trading strategies. As the AI ecosystem evolves, financial institutions must stay updated on the latest AI applications and customer needs by continuously improving their AI systems.

AI is set to transform various aspects of the financial sector, including banking, investments, and insurance. However, like all rapidly evolving technologies, AI also presents new challenges and concerns. Key issues such as regulation, ethics, and workforce changes will need to be addressed moving forward. Governments and regulators must find a balance between fostering innovation and protecting consumers from potential abuses or unintended consequences. 

Implementing AI systems in the highly regulated finance industry involves significant logistical and compliance challenges. Financial institutions must carefully manage AI projects to ensure data quality, security, and adherence to regulations. A major hurdle is obtaining clean, representative data to train AI models. Since models are only as effective as the data they are built upon, financial institutions must establish robust data governance processes. However, many banks have complex, fragmented data architectures that span decades-old mainframe systems. Connecting and preparing this data for AI projects requires considerable effort.

Artificial Intelligence, Business & Entrepreneurship, Generative AI, Industry Perspectives, Technology & Digital

Top AI Trends Transforming Digital Marketing

Nehatrendy

Kicking-off Digital Marketing Strategy

According to leading research reports, 35% of businesses adopting AI, indicating that more than one in three companies are investing in AI for transformation, innovation, and competitive advantage. In the ever-expanding digital marketing landscape, businesses face the challenge of navigating vast amounts of data, identifying trends, and delivering targeted content to their audiences. Artificial Intelligence (AI) has quickly become essential in reshaping the digital marketing field, fundamentally changing how businesses approach their promotional efforts. AI tackles these challenges by offering robust data analysis, predictive analytics, and automation capabilities. As a result, marketers can make data-driven decisions, refine their targeting, and optimize campaigns with unprecedented precision.

A global survey of early AI adopters shows that AI applications are focused on marketing-oriented goals. Beyond impressive statistics, the real impact of AI lies in the subtle transformations it brings to campaigns, customer experiences, and overall marketing strategies. Organizations leveraging AI are skillfully navigating the data landscape, strategically deploying AI to gain a competitive edge, and constantly innovating, and enhancing customer experiences.

AI in digital marketing works behind the scenes in two main ways. First, on the business side, it helps predict customer desires and optimizes ad buying. Second, on the customer side, AI enhances the experience—think personalized recommendations or quick responses to inquiries. AI acts as the brain behind marketing teams, meticulously analyzing vast datasets, which are the currency of the digital marketing realm. This data-driven approach allows AI to develop intelligent and effective marketing strategies.

Latest Trends on How Marketers are embracing AI

When we analyze the differing perspectives of various groups of marketers—such as freelancers, in-house marketers, and agency marketers—some interesting patterns emerge. This enthusiasm is likely driven by AI tools’ anticipated time-saving benefits and their relative ease of implementation for marketers. AI technology deserves attention in digital marketing due to its tangible benefits, which outweigh the associated risks. Some of these benefits include:

  • Cost Reduction: 54% of businesses reported a decrease in costs.
  • Enhanced IT and Network Performance: 53% experienced improvements in their IT and network performance.
  • Improved Customer Experiences: 48% noted that AI contributed to happier customers through enhanced experiences.

Emerging Themes in the Future of Digital Marketing

AI technology has the potential to revolutionize digital marketing by enhancing personalization, productivity, and overall effectiveness. As AI advances, new applications in digital advertising are likely to emerge. Here are some key areas where AI is significantly impacting the field:

Personalization: AI algorithms can adjust content based on customer browsing behavior, demographics, and preferences. This enables marketers to tailor content, offers, and advertisements. It improves user experience and allows for more targeted and effective marketing campaigns. Ultimately, this boosts conversion rates. The personalized approach ensures that each user receives messages suited to their specific interests.

User Journey Analysis: Understanding where users are in their journey is essential for effective content targeting. AI excels at analyzing user behavior and identifying the specific stage of the buying process a customer is in. Users can be in the research phase. They can also be ready to make a buy. AI provides tailored content that guides them seamlessly through the sales funnel.

Digital Marketing Campaigns: AI-driven chatbots play a crucial role in supporting digital marketing campaigns. They provide personalized customer support, gather data for lead generation and analysis, and enhance engagement and customer satisfaction. AI’s involvement in content marketing begins with predictive content optimization. By harnessing predictive analytics, AI can analyze extensive datasets to discern patterns and forecast user behavior.

Customer and Performance Analytics: AI algorithms help marketers gain a deeper understanding of their customers. Machine learning can analyze vast amounts of data from social media, website analytics, and customer feedback, providing valuable insights. AI’s analytical capabilities extend to measuring content performance. Algorithms track key metrics such as open rates, click-through rates, and social media engagement. With this data, marketers can refine their content strategies iteratively.

Automatic Content Generation: AI technology assists marketers in saving time and enhancing content quality. By utilizing data and user preferences, AI-powered content creation tools can generate more engaging and relevant blog posts and social media content, leading to improved campaign performance. AI addresses the previously time-consuming task of content creation by introducing automated tools that leverage natural language processing and machine learning algorithms to produce high-quality written content and visuals.

Images and Videos: In the realm of images and videos, AI can recognize objects, people, and other elements. This capability enables automated tagging, personalized search functionality, and creative inspiration, enhancing the visual aspects of digital marketing.

Risk Management: AI algorithms are proficient in detecting anomalies. By utilizing predictive modeling, analyzing text-based data, monitoring real-time activity, and assigning fraud scores, AI helps businesses identify and prevent fraud in their digital marketing activities.

AI in Advertisement

Artificial Intelligence (AI) is a transformative force that enhances the precision and effectiveness of targeted advertising. By thoroughly analyzing large datasets, AI identifies complex patterns in customer behavior, purchasing habits, and demographics.

Real-Time Optimization: In the fast-paced world of advertising, the ability to adapt in real-time is crucial. Real-time optimization is a key feature that allows continuous monitoring of campaign performance. AI can make instant adjustments to targeting or messaging based on changing dynamics, significantly improving the overall effectiveness of advertising strategies.

AI-Driven A/B Testing: AI also enhances A/B testing by introducing greater efficiency and depth to the process. AI algorithms manage the distribution of traffic across different web pages or ad variations during the testing phase. This automated approach not only saves valuable time but also ensures a randomized testing environment free from biases. By utilizing machine learning, AI can identify patterns from A/B tests, which informs strategic decisions for future experiments. Recommendations on aspects like color schemes and fonts, based on audience segmentation, highlight AI’s valuable contributions.

Efficiency Boost in Campaigns: Properly applying AI technology can significantly enhance the efficiency of digital advertising campaigns. By automating tasks such as audience segmentation, content creation, and performance analysis, AI allows advertisers to concentrate on strategic decision-making. These efficiency gains lead to campaigns with higher conversion rates, as resources are used optimally to achieve marketing goals.

AI in Customer Relationship Management (CRM)

AI in Customer Relationship Management (CRM) carefully processes and interprets user behavior data. Additionally, real-time optimization through access to marketing data enables marketing teams to quickly retrieve, analyze, and act on critical information. This faster pace enhances the agility of marketing campaigns, allowing businesses to respond promptly to market dynamics and consumer trends.

Accurate Sales Forecasting: AI-driven CRM systems significantly improve the accuracy of sales forecasts across various markets. By analyzing historical data, market trends, and consumer behavior, AI generates forecasts with remarkable precision. This enhanced accuracy allows businesses to optimize resource allocation, maintain an efficient supply chain, and make informed strategic decisions.

Intelligent Customer Record Organization: AI in CRM acts as a sophisticated organizer of customer records, managing a wealth of information with precision. This includes contact details, demographic insights, communication history, and purchase records. The result is a comprehensive view of each customer, enabling businesses to identify sales opportunities, tailor marketing strategies, and deliver personalized customer experiences.

Streamlined Customer Inquiry Response: The integration of AI-powered chatbots transforms the way customer inquiries are handled. These bots, enhanced by natural language processing, efficiently categorize incoming inquiries. Urgent questions receive immediate attention, reducing dissatisfaction and improving the overall customer support experience.

Conclusion

As the digital marketing landscape experiences a significant shift, the statistics highlight important trends. The diverse comfort levels expressed by freelancers, in-house marketers, and agency professionals provide valuable insights into the different approaches to AI adoption. Despite these differences, the overall sentiment is optimistic, with a majority feeling comfortable integrating AI into their campaigns.

However, AI in marketing also presents several challenges, particularly concerning data, that marketing teams should consider as the technology evolves. Firstly, there is the issue of transparency. How should content marketers disclose which materials, such as industry reports or blog articles, have been generated either partially or entirely by AI? What regulations exist to guide businesses in informing customers about data usage? Secondly, intellectual property poses challenges when AI systems utilize the intellectual property of third parties to generate new content without proper attribution. Thirdly, there are legal and ethical concerns. As AI systems often handle vast amounts of data, it’s crucial to consider how customers’ personal information is stored and used to prevent security breaches. Lastly, data quality concerns arise because some AI systems are trained on historical data, which may introduce biases or inaccuracies in their outputs.

Looking ahead, the path is clear: AI is not just a passing trend but a fundamental force shaping the present and future of digital marketing. Businesses that adapt to this changing landscape can gain a competitive edge by leveraging the efficiency and insights AI offers. As these tools evolve and become more sophisticated, we can expect widespread integration of AI throughout the digital marketing realm, leading to a new era of strategic optimization and audience engagement.

Artificial Intelligence, Generative AI, Industry Perspectives, Technology & Digital

Predictive Analytics Powered by Artificial Intelligence

Nehatrendy

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Artificial Intelligence, Generative AI, Industry Perspectives, Technology & Digital

Natural Language Processing (NLP): a Machine Learning and Artificial Intelligence perspective

Nehatrendy

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