Discover The Power of AI and ML in Finance

In the quiet technological revolution sweeping across sectors, Artificial Intelligence (AI) and Machine Learning (ML) hold the pole position. These twin engines of change are propelling speedier functions, sharper predictions, and deeper insights in various areas. Intriguingly, these advancements don’t stop at tech-buzzing spheres like robotics or software development; they extend their prowess into diverse fields. Among them, few have experienced as much impact as finance — a traditional domain now being reshaped by this potent partnership— AI and ML in Finance.

So grab that steaming cup of coffee and buckle up for an exciting journey as we delve into the power unleashed when cutting-edge technology meets high-stakes finance.

What is Artificial Intelligence (AI) and Machine Learning (ML)?

It’s quite likely you’ve heard about AI more than once. It’s become something of a buzzword, hasn’t it? Yet for all its popularity, defining what exactly it means can be exasperatingly elusive! But let’s streamline things here.

Artificial intelligence refers to computer systems designed to mimic human intelligence with remarkable accuracy. Simply put, it’s building machines that replicate — even exceed — thinking patterns and behavior attributes displayed by homo sapiens. From problem-solving capabilities to natural language processing and understanding, from perceptive judgement calls to sophisticated learning undertakings – AI has become increasingly adept at performing tasks commonly associated with intelligent beings.

Machine Learning, on the other hand, is often viewed as a subset of AI but packs power beyond measure in its own right. Yes indeed! ML offers pivotal contributions toward realizing those lofty dreams outlined under artificial intelligence – through data driven experiences illuminating paths forward instead of laboriously pre-programmed routes.

Imagine a toddler learning how to walk: observing others walking around him while simultaneously trying steps himself allows our little explorer to gradually master his locomotion skills over time! Machine Learning mirrors that process: it’s about gathering data, recognizing patterns and then making informed decisions or predictions based on those findings.

Now, how do AI and ML relate to finance? Well, that’s an enticing question. Read on to uncover some fascinating applications of this dynamic duo in the world of finance!

Applications of AI and ML in Finance

The use of artificial intelligence for finance has expanded exponentially, transforming the financial industry almost completely. There are numerous ways that artificial intelligence in finance is leveraged to achieve a vast range of goals.

Financial Monitoring

Financial organizations frequently use machine learning in finance to efficiently monitor their financial systems. This involves important tasks like:

• Detecting fraud
• Tracking user behavior patterns
• Identifying suspicious activities

By tracking these aspects of financial system continuously, anomalies can be flagged for review which enhances overall security measures. With its predictive capabilities, ML provides insights that make financial monitoring more efficient than ever before.

Making Investment Predictions

Artificial Intelligence’s ability to process and analyze large amounts of data quickly makes it valuable for making investment predictions. Not only does this simplify the task at hand, but it also increases the accuracy of such predictions – a dream come true for investors!

Traditional methods often miss out on crucial potential influences or changes due to human limitations. While there still exist many unknowns in the market fluctuations, algorithmic trading with AI and other ML methods significantly reduces risks by basing decisions on comprehensive analyses.

Process Automation

In most business realms, including the finance sector, automation is key. Particularly streamlining daily operations leads to increased efficiency and improved outcomes.

AI/ML use cases extend far beyond comprehension with respect to process automation; Ranging from executing trades seamlessly within minutes (as seen in high-frequency trading) to customer service operations being handled swiftly through chatbots without needing human intervention.

Secure Transactions

Online transactions have grown dramatically owing to technological advancements. However, they’ve become susceptible targets for cybercriminals.

Luckily, we’re now able to safeguard digital platforms by utilizing machine learning algorithms which continually learn from previous fraudulent attempts made henceforth enhancing transaction safety.

Risk Management

Risk has always remained a constant challenge within the financial sector however, the AI’s incorporation elevates the ability to manage it. By analyzing past trends and changes in market variables, ML in finance is now capable of predicting potential risks with an impressive degree of accuracy.

Algorithmic Trading

AI influences algorithmic trading by using complex algorithms that make trades based on preset conditions. The advances in financial machine learning allow us these models to observe patterns over time, identify patterns & make predictions leading to effective ‘buy’ or ‘sell’ trading decisions faster than any human trader could even perceive them.

Financial Advisory

The rise of Robo-advisors delivering financial advice backed by data inputs and by algorithms- sure signifies the era of AI finance! These highly-efficient advisors provide users with portfolio management strategies tailored specifically for their needs based on extensive data analysis eliminating possibilities of human errors entirely.

Customer Data Management

In today’s world valuable data is being generated at such a ridiculous rate that managing it effectively poses a significant challenge. Here customer data management benefits vastly from artificial intelligence through AI-enabled tools which can collect, analyze as well as segmentize consumer behavior helping companies tailor their products and services smartly according to observed preferences.

Decision Making

Lastly, decision making – which has always been important yet prone to error when done purely by intuition or incomplete data; It can now be informed decisions made with conviction courtesy of AI and ML in Finance. They have brought about a revolution where deep learning in finance yields useful insight reducing uncertainty resulting generally higher data quality & informed strategic choices.

Benefits of AI and ML in Finance

The advancements in technology, particularly artificial intelligence (AI) and machine learning (ML), have substantially influenced many sectors around the world. The finance industry is a noteworthy beneficiary as these technologies bring forth several benefits to financial services firms ranging from enhanced security to improved customer service and increased efficiency.

Enhanced Security

Without any room for doubt, AI has emerged as an excellent tool for fortifying financial security. It aids in establishing foolproof systems by spotting anomalies which humans may overlook. Moreover, the use of various machine learning techniques in finance contributes to detecting fraudulent transactions by recognizing patterns deemed suspicious or out of order.

Studies like Predicting financial fraud using machine learning indicate how machine learning algorithms can forestall probable frauds timely. Also, generative ai in finance helps simulate scenarios to test systems against potential risks hence strengthening security measures immensely.

In recent years, there is a mention-worthy rise in investments made into cybersecurity reflecting on the significance accorded to secure operations and transactions. Let’s delve into how artifical intelligence aids enhancing the customer experience and service next.

Improved Customer Service

Artificial Intelligence has proved its competence as an exceptional performer when it comes to improving customer service experiences across industries, including financing online banking. It eases tasks such as automation of responses for recurring queries also known as FAQs with the help of chatbots programmed by deep learning mechanisms

Furthermore, they offer personalized advice leveraging various AI/ML use cases thereby delivering tailor-made solutions at lightning speed. Additionally, AI optimizes big data analysis instrumental for gaining insights on client behavior and preferences contributing efficiently towards target marketing efforts.

Using these techniques companies can provide superior levels of service tailored directly certain individuals’ needs making them more likely remain loyal customers because feels their unique requirements are being met efficiently competently without unnecessary delays misunderstandings

Future Prospects of AI and ML in Finance

Artificial Intelligence (AI) and Machine Learning (ML) are becoming increasingly integrated into the finance sector. The possibilities brought forward with these technologies seem limitless, which is why it’s essential to understand their future applications. These include enhancing recommendations or sales of other financial services and products, advancing customer sentiment analysis, and delivering better customer service.

Recommendations or Sales of Different Financial Products

Predicting customer behavior has been a constant challenge in finance ai solutions; however, AI and ML now make this a more accessible objective. By collecting and analyzing large amounts of data from customers’ buying habits, preferences, and interactions usingMachine Learning in finance, companies can generate tailor-made product recommendations that maximize both revenue for the firm and value for the client.

As we progress further into the AI age within finance artificial intelligence tools like generative AI will be able to create marketing strategies specifically designed based on individual customer data. This individually-targeted approach can greatly enhance the effectiveness of upselling or cross-selling efforts by big data finance and insurance firms themselves, resulting in increases in overall sales performance.

Customer Sentiment Analysis

Another important application area for ai/ml use cases lies magnificently ahead: customer sentiment analysis. By employing sophisticated Machine Learning algorithms on social media posts, comments, reviews & other online content related to a company’s products or services – an accurate picture of how customers feel about them can be obtained. Such information allows firms to identify potential issues early on before they exacerbate while also highlighting areas of customer engagement where they’re surpassing expectations.

This smarter use of AI in Finance enables businesses to anticipate changes in market average public opinion quickly and adapt their strategy accordingly. It provides valuable insights into cleaning and shaping transactions for aggregation impacting profitability positively.

Better Customer Service

Lastly but ultimate goldmine awaiting sincere miners is significantly improving customer service levels through the intelligent application of ml finance tools . Live chatbots armed with ever-advancing deep learning capabilities can effectively answer customers’ questions 24/7. They now exceed just being able to handle simple queries, extending to solving complex financial issues or offering tailored financial advice.

Implementing AI and ML in Finance customer service operations is a cost-effective way for financial services companies to provide accurate & immediate support around the clock while freeing up human resources for more critical tasks.

The bridge between artificial intelligence for the corporate finance, and superior customer service seems destined to become narrower moving ahead, bringing in positive incremental changes benefiting businesses & clients alike.

Required Skills for AI and ML Professionals in Finance

Working with finance artificial intelligence isn’t solely about knowing how to code; it’s also equally important understanding its specific business objectives and applications especially contextualized for your industry sphere. Some core competencies expected from any ai finance professional typically include:

• A solid understanding of mathematics/statistics, and algorithms.
• Programming proficiency in languages like Python and R.
• Familiarity with AI & ML tools (Tensorflow, PyTorch).
• Sound understanding of financial markets is paramount.
• Ability to translate complex data into actionable business strategies.

An absolute mastery over these areas would encourage more companies aiming at harnessing the generative potential of ml in finance leveraging your skills and expertise. Remember, it’s not only about how well you use these technologies; but even more so, how strategically you can apply them to resolve real-life industry challenges.

In my opinion, as long as there’s a willingness to learn and adapt with this ever-changing technological landscape coupled with primary focus on problem-solving – Success is definitely within reach! I say, go ahead–dive right into machine-powered intelligent future of finance!

The Role of AI in Accounting

Unveiling the Sophistication of AI-driven Accounting

Grasping the contemporary essence of accounting requires a fundamental understanding of how artificial intelligence is contributing to its remodelling. As the finance sector embraces advancements, AI in accounting emerges as a game-changing enhancer. It elevates efficiency and precision – transforming complex tasks to be carried out with exceptional speed and minimal human intervention.

Artificial Intelligence has pioneered innovations in several domains within accounting, like auditing, payroll management, and tax preparation. For instance, rather than relying on traditional means of bookkeeping that are prone to human error, businesses increasingly opt for AI-enabled software that meticulously keeps track of every financial transaction.

Key Areas where Artificial Intelligence Transforms Accounting:

A rapid shift towards ‘machine finance’ highlights four key areas where AI is shaking up accounting and business processes through automation and predictive capabilities:

• Invoice Processing: Levering AI implies automatically capturing invoice details, verifying them against predefined standards and accelerating the approval process – all swift and error-free.
• Expense Management: Automation aids instantaneously sort-out business expenses from personal ones—monitoring costs without posing burden over employees.
• Payroll Systems: An uninterrupted/automated data flow achieved through AI reduces administrative duties by calculating pay/taxes while adhering to compliance requirements.
• Fraud Detection: Machine Learning tools can detect unusual patterns or anomalies in transactions – serving as an additional layer of security against fraudulent activities.

In this era moulded by digitally-controlled environments, embracing “AI Finance” appears more imperative than ever before.

Where Traditional Practice meets Future Tech

With an eye toward innovation, my belief is that financial professionals should be learning about these advances in financial data science and financial Machine Learning – whether it’s reading finance PDFs or engaging with thorough coursework related to artificial intelligence for finance.

It’s not just about replacing manual approaches but enhancing traditional practices with the technological capabilities of AI. This integration of old and new forms a potent tool for accountants, making them more equipped to handle the rigours and complexities that modern finance brings.

In acknowledgment of this trend, cutting-edge software companies have accelerated their efforts to integrate AI into accounting systems. Hyperscience, with its key focus on machine learning technologies, is one such company transforming this landscape.

The Road Ahead

With substantive impacts already seen in sectors ranging from automating mundane tasks to predicting future trends based on intricate algorithms – it’s evident that the role of artificial intelligence in accounting will only continue expanding. The infusion of AI in finance has made accounting not just an industry friendly but also future-ready!

The journey remains ongoing, and as this ‘AI Finance’ revolution continues to unfold, the realm of accounting promises even more efficient, transparent and innovative practices down the road.

Delving into the realm of finance, numerous sectors have started to feel the ripple effects of Artificial Intelligence (AI), including Financial Planning and Analysis (FP&A). The integration of AI technologies plays a pivotal role in augmenting traditional financial planning processes, embedding efficiency, accuracy and predictive prowess within them.

Predictive Analytics

Interestingly, one facet where AI has truly revolutionized FP&A is predictive analytics. Machine Learning offers significant improvements over traditional statistical models by operating on large datasets and processing multiple variables simultaneously. It can meticulously forecast revenue trends, expense patterns, and cash flow scenarios that would typically require many hours when done manually.

For instance; “Generative AI” in finance can synthesize vast amounts of historical data to predict future results accurately. With machine learning at its core, this tool offloads tiresome tasks from analysts’ shoulders while providing data-driven insights for decision making.

Streamlined Forecasting Processes

Moreover, AI in financial planning makes forecasting an almost effortless endeavor. Previously dependent on uncertain economic indicators and educated guesses, this process now thrives on an ML algorithm’s propensity for mining recurring patterns within significant data volumes – hence named ‘machine finance.’ By mapping out intricate relationships between various parameters affecting corporate performance in real-time, organizations can make dynamic adjustments to their plans without hiccups.

Automated Reporting

Artificially inflated reports characterized by redundant information are becoming a thing of the past thanks to AI solutions. They ensure optimal content relevance while availing reporting tools with near-perfect correctness – quite unlike what we’d expect if humans were solely responsible for these tasks. Cleaning up and shaping transactions for aggregation fosters better understanding across all business hierarchies through simple dashboards showing easy-to-absorb visual representations garnered from complex, data sets.

Given these advances in financial Machine Learning technology evident in enhanced FP&A processes, it isn’t surprising that studies highlight robust implementation rates amongst discerning firms prioritizing strategic growth through innovation.

In conclusion, the influence of AI and ML in Finance, especially in Financial Planning and Analysis, is profound. Delivering predictive capabilities, data analytics, streamlined processes, and automated reporting mechanisms – businesses get hands-on with enhanced tools for data-driven decision making. With such advances perpetuating future growth trends within this industry vertical, I believe it’s safe to predict that AI’s impact in FP&A will remain transformative for a considerable time to come.

The Role of AI in Procurement

Artificial Intelligence, better known by its acronym ‘AI’, is making waves in the realm of procurement. As one of the growing themes within finance, it brings to central focus how this evolving technology can bring efficiency and significant improvements to businesses’ procurement processes.

Let’s delve into understanding the true role that AI plays in helping streamline the procure-to-pay cycle, optimizing supplier relations, and bringing effective cost-saving strategies into play.

Streamlining Procure-to-Pay Cycle

AI has a significant hand in streamlining what is termed as the ‘procure-to-pay’ cycle. Whether by improving workflows or cutting down tedious manual tasks, AI elevates proficiency. For instance, AI can automate processing invoices which will result in increased speed and reduced chances for errors.

Suppliers Relationship Management

Next comes suppliers relationship management (SRM), an area where AI shines bright. It facilitates learning from past interactions, behavior patterns from different suppliers over time. Thereby making future dealings efficient with improved resource allocation. Supplier-related risks could also be minimized using predictive analytics – an aspect of “ai finance.”

Implementing Cost-Savings Strategies

The powerful trio of AI-ML-Finance not only enhances operations but also aids in implementing cost-saving strategies. This unique blend provides actionable insights allowing firms to capitalize on market trends and induce negotiation power with vendors – a vital practice when crafting competitive pricing arrangements.

But it doesn’t stop here; these are just seizing the surface of possibilities offered by AI in procurement space.

Looking ahead, evolving technologies like deep learning in finance promise further advancements – even more precise predictions for credit scoring and personalized vendor recommendations based on real-time data analysis.

Indeed at every stage – right from managing requisitions to final invoice clearance – artificial intelligence for finance is redefining norms while setting higher standards for financial efficiency and process automation.

With such clear benefits, I encourage companies yet untouched by AI, to reconsider their stance. As we anticipate ‘artificially inflated’ adoption rates, one can’t deny the pivotal role of AI in turning procurement from a transactional process to a strategic function.

Let’s keep our eyes on this exciting transformation, as it’s just the beginning of what AI and ML can accomplish in finance.

Cloud-Based Finance Solutions

When it comes to unlocking the potential ofAI and ML in Finance, cloud technology plays an integral role. Leveraging cloud infrastructure allows financial institutions to process vast amounts of data at unprecedented speeds. As we delve deeper into this exciting junction of advanced tech and fiscal service management, let’s explore some key aspects that make cloud-based solutions essential for exploiting AI and ML.

Seamless Integrations

One might wonder—why turn to the clouds with powerful Machine Learning tools at our disposal? Well, imagine your local storage as a lantern and cloud computing as a blazing sun—when it radiates, every nook and corner is illuminated alike! With cloudy infrastructure, you get seamless integrations with AI/ML use cases in the blink of an eye. It results in holistic financial analyses that are more accurate than ever before.

Evolution Through Innovation

Cloud-based solutions aren’t only about extensive coverage—they also bring innovation to your fingertips. Offering a fertile ground for incorporating deep learning in finance, these solutions allow firms to dabble with modern capabilities while making traditional banking processes increasingly redundant.

For instance, one could impart artificially inflated intelligence to their systems for better understanding market trends or creating predictive models around hyperscience stock prices—an aspect once unimaginable without human intervention.

Data Management

Reliable, efficient data management is another feather added by such platforms to the hat of any financial institution. Cleaning and managing assets using data scientists shaping transactions for aggregation—a critical aspect within the realm of big data finance—is achieved effortlessly when operations are driven on a robust cloud interface.

So whether it’s tapping into AI-powered anomaly detection or using generative AI in finance-related operations like market simulations—all becomes feasible with proper data governance ensured by these radical tools!

Striking a balance between technological prowess and user-centered design, advanced cloud-based technology showcases potentially transformative impact on sectors across the board—especially those as intricate as finance! So here’s to revolutionizing the AI finance world, one cloud platform at a time!

Leading the Future of Finance with AI and ML

Revolutionize your perspective on the future of finance, which is inextricably linked with two powerful technological tools: Artificial Intelligence (AI) and Machine Learning (ML). In essence, artificially inflated concepts are transformed into practical applications when seen through the lens of ml finance. We’re not talking about a distant idea here; this transformation is happening right now.

The digital age has pushed financial companies to innovate aggressively than ever before. Top market players have moved beyond traditional methods, embracing AI and other ML systems to streamline operations and make informed decisions.

When we peek at the intersection of finance ai and artificial intelligence, we see promising use cases. Let’s delve into some key ways by how they’re upending the financial services industry and norms:

1. Predictive Analytics: Through Machine Learning in finance, firms can analyze patterns in large datasets much faster than any human could – leading to insights that enable predictive analytics. For instance, customer behavior trends can be forecasted accurately to craft data-driven marketing strategies.
2. Risk Assessment & Management: Identifying potential threats or uncertainties is fundamental in finance. Leveraging AI/ML algorithms with big data finance records helps pinpoint outlier trends, unusual transactions or behavior indicating risk.
3. Personalized Financial Services: Modern consumers appreciate personalized experiences more than ever before. With effective utilization of AI in finance aifinance solutions become tailored to individual customers’ needs—enhancing engagement rates, increasing loyalty while maximizing satisfaction levels.
4. Regulatory Compliance: The intricacy of global regulatory standards makes it hard for financial institutions to remain compliant consistently. However, implementing AI platforms could help navigate these complexities via automated updates on changing rules around cleaning and shaping transactions for aggregation—an aspect gaining popularity recently.

Therefore, understanding all facets of this transformative shift becomes imperative whether you’re an investor contemplating where the market is headed or a professional maneuvering their career path towards being an ai finance expert. As time progresses, it’s evident that AI and ML in Finance are no longer optional—they’re obligatory to spearhead advancement in this fast-paced sector. The future undoubtedly belongs to those who lead the reinvention—utilizing AI and ML today to pave way for a prosperous tomorrow.

Harnessing Native AI with Workday Solutions

Harnessing the power of AI and ML in Finance can often seem daunting, especially given its complexity. However, solutions such as Workday’s native AI make it remarkably more accessible and practical for financial institutions. But what exactly is this about, and how does it streamline operations within the finance sector?

Workday boasts impressive native artificial intelligence capabilities that can significantly improve productivity. By using Machine Learning algorithms, Workday can automate routine tasks that would otherwise consume the valuable time of staff members. This includes cleaning and shaping transactions for aggregation, which is an important aspect of data management in finance.

On top of making operations more efficient, Workday’s native AI has proven to be a game-changer when dealing with big data in finance domain. With an ability to scan through thousands, even millions of data points quickly, these solutions offer precise insights to guide decision-making processes. It provides something akin to having a dedicated team analyzing your finances around-the-clock – only faster and devoid of human error.

Possessing features like predictive analytics also empowers organizations to foresee future trends similar to the promise held by deep and Machine Learning works in finance. From anticipating cash flow fluctuations to detecting possible security threats before they occur – I find that incorporating this type of technology into business strategy injects confidence into financial planning.

Indeed, harnessing native AI with Workday offers significant benefits not just on an operational level but also strategically. As we look forward, advancements like these resonate with greater emphasis on efficiency and intelligent automation – driving unprecedented transformation in the world of finance artificial intelligence.

All things considered; it is clear that leveraging sophisticated tools like Workday’s native AI goes beyond enhancing productivity in the workplace – it takes us a substantial stride forth towards leading the future of Finance with AI and ML.

Understanding the Impact of AI on Financial Markets

If you look back a couple of decades from now, one could hardly imagine that terms like artificial intelligence for finance would become commonplace in our everyday discourse. Fast-forward to present day and we find ourselves navigating a global financial landscape heavily influenced by AI and ML (Machine Learning). Let’s dive into understanding the substantial influence these technologies have on financial markets.

AI influences financial markets primarily through speed, scale, and sophistication. For instance, High-frequency trading (HFT), an area where AI in finance has made significant inroads, relies heavily on the speed at which decisions are made and trades executed.

In terms of scale, finance artificial intelligence has opened pathways for big data analysis that were previously unimaginable. Deciphering multitudes of data points generated every second becomes feasible with AI application. This ability to process big data, known as “big data finance”, helps organizations make far more informed decisions about investments and risk management.

When it comes to sophistication, nothing beats Machine Learning in finance. Advanced algorithms can learn from past behavior and adapt their predictions accordingly — improving both accuracy as well as reliability over time.

This synchronization of speed, scale input data, and sophistication brings an unparalleled potential to reshape how the financial market operates completely. We see its impact across all essential functions including trading strategies where ML-powered platforms can automate trades without human input; algorithmic trading precisely is a perfect example here.

Algorithmic Trading with AI and ML

When we delve into the domain of algorithmic trading, the utility of artificial intelligence (AI) and Machine Learning(ML) becomes exceptional. Today, many financial institutions are employing AI and ML to create automated systems capable of buying and selling securities promptly.

Trading systems harnessed by AI/ML use cases have scripted a new narrative in finance. Here’s how they work: first, these models absorb substantial amounts of information obtained from various finance pdf and big data finance data sources. Following this, useful insights are extrapolated through Machine Learning in finance processes.

One extravagant feature unique to these automated systems is their ability to learn independently. They progressively fine-tune their strategies based on market trends, displaying evidence of what I like to call ‘generative ai in finance.’ This aspect strengthens accuracy over time, enhancing overall profitability.

Employing AI and ML into algorithmic trading assures transactions’ speed unmatched by human traders. This swift action creates an artificially inflated advantage as every millisecond matters in high-frequency trading – imagine beating your competition just because you blinked slower!

Algorithmic trading helps manage such large volumes of orders far more efficiently than manual methods can achieve. The process breaks down sizable orders into several smaller ones exploiting the best available prices across multiple exchanges simultaneously – tediously impossible for humans but a breeze for machines.

The Regulatory Environment for AI and ML In Financial Services

There’s one aspect that can’t be ignored while talking about AI and ML in Finance, and that is the regulatory environment. It plays a crucial role in determining how these revolutionary technologies are employed within the financial sector.

Presently, regulators global banks worldwide grapple with the challenge of developing policies that promote innovation without compromising security or customer protection. They’re trying to strike this delicate balance and adapting to the evolving landscape driven by AI/ML use cases across various sectors of finance.

Definitely, finance artificial intelligence is redefining norms, rules, protocols and introducing newer methods into systems. However, my viewpoint here shouldn’t serve as an implication of laissez-faire from regulatory bodies towards such developments. Contrarily, it has propelled these entities into action for establishing better frameworks to maintain decorum and foster beneficial growth.

truly exciting era we’re stepping into; let’s maneuver thoughtfully with due diligence for making most out of it!