What is artificial intelligence applications in financial services?

As such, artificial intelligence applications in financial services revolutionizing the industry through new opportunities for optimization. Over the recent past, adoption of the technology has become widespread among the financial institutions departments, banks, and insurance firms.

This is because AI has endless possibilities of improving the finance industry. They can replace manual operations, forecast market trends and decrease the opportunities of human mistakes. However, as with any new concept, it comes with some limitations, for example, issues to do with data privacy, and high costs of putting into practice.

Why does AI matter in finance?

AI makes financial institutions competitive. It enhances quality, productivity and satisfaction of the end-use customer. What choices do you make regarding your money that AI can improve?

How AI Used in Finance?

AI is applied in the financial sector to improve productivity and to advance innovation. The promotion of innovations helps the business to solve various problems related to the automation of work and the improvement of decision-making, which ultimately contributes to the increase in profits and the growth of customer satisfaction.

Algorithmic Trading:

High frequency trading is a type of trading that implements AI to make trades during the best time possible through analysis. It strengthens trading plans and optimizes markets; these are valuable to investors.

Automation and Efficiency:

AI relieves people from doing monotonous work like typing, entering data, etc., and also minimizes errors. It increases productivity by enabling the financial professionals to do more important and engaging work.

Competitive Advantage:

AI help financial firms stand out from the competition by allowing them to act more quickly and decisively. This is especially important because the financial market does not wait for any business or company to rest and therefore, the need to work smart by using this software renders any business or company ahead of the others within a very short time.

Compliance:

AI enables financial sectors to remain in compliance with the legal norms of the countries where the firms operate. It also helps to minimize the cases of overlooking some laws by recommending action on the checklists prepared by experts.

Credit Scoring:

AI improves credit scoring models by using more data from sources other than the person’s reported credit history. Consequently, the credit risk assessments will be more reliable for both the lending company as well as the borrower.

Cost Reduction:

The efficiency of business processes and AI means that operative costs are minimized throughout the organization. The use of the blockchain means that the financial institutions can work better, reducing on overhead costs.

Customer Service:

Currently, AI-based chatbots offer round the clock customer support, answering questions and addressing concerns. They enhance customer satisfaction due to quick queries’ resolution and customization.

Data Analysis:

They use a lot of financial information and provide useful information to the financial world. This is because helps companies in decision making, in screening trends and in foreseeing future market movements.

Fraud Detection:

Real-time identification of fraudulent activities is only possible with the help of AI. It looks at trends and outliers in transaction information and identifies potentially unlawful activities, to combat fraud.

Loan Processing:

By using AI, loan processing is fast enhanced because applicants are automatically evaluated. It has the effect of decreasing the cycle time for loan approvals and is more effective.

Personal Finances:

AI Apps improve the financial stewardship of people. These tools help to provide an individual plan for investing and expenses, show how to save money and how to invest it.

Portfolio Management:

Portfolio management is supported by AI through market data analysis of the portfolio and trends forecast. It assists the investors in coming up with good investment portfolios based on their returns’ preference and their capacity to take risks.

Predictive Analytics:

Here, predictive analytics is used to predict the future market trends and customer behavior profile. This makes it possible for financial companies to act strategically and have proper preparations for the future problems.

Risk Management:

Risk management is improved by the use of AI since it is able to analyze data for risk indicators. It serves the purpose of reducing risks by allowing financial institutions to prevent risks by using the analytical services provided.

Sentiment Analysis:

AI interprets data acquired from the polls and social media in order to predict tendencies in the market. It assist the finical institutions to determine the sentiment of the public in relation to certain investment or the market in general.

Customer Engagement:

AI makes customer interactions personal and offers the right information to customers. It enhances the relationship between suppliers or buyers and hence the loyalty of customers hence being of benefit to the business.

Fraud Prevention and Risk Assessment:

Using AI, suspicious activity is identified as a key component to preventing fraud. It also continuously evaluates the risk to help businesses respond to it appropriately and timely.

Personalized Banking Services:

AI provides specific banking solutions based on the customers’ information. It offers financial solutions, and thus could assist the client in making correct choices depending on his or her circumstances.

Automated Document Processing:

AI helps in reducing document handling time as it involves separation and categorisation of data. This is time-saving, reduces on mistake, and has a positive effect on the viability of financial operation.

Market Forecasting:

Market forecasting is achieved through AI where it uses the historical information to try to look for trends. It helps financial specialists in forecasting and, therefore, in changing the necessary measures.

Cybersecurity:

AI improves cybersecurity because it can detect danger and avoid harm. It preserves financial data values by making protective actions when there is an assumption of a breach.

Stress-Testing:

AI enhances the stress testing by mimicking different markets situations. Thus, it enables financial institutions to evaluate their ability to cope with economic volatile conditions and plan for the risks a head.

Key Stakeholders of AI in Finance:

By detailing out the different areas of financial services that AI affects, one realizes that virtually all the stakeholders who shape the industry have a say in how the technology is deployed. Such stakeholders ensure that the AI systems produced meet the legal requirements and promote effective financial management.

Auditors and Internal Control Teams:

The auditors and the internal control teams make sure that the Artificial Intelligence systems developed meet the required legal requirements and security measures from the market. They conduct some checks for risk and non-opaque operations for the artificial intelligence system.

Chief Information Officers (CIOs) and Chief Technology Officers (CTOs):

AI strategies in the financial institutions are strategic directions that are normally coordinated by CIOs/CTOs. They approve the integration of the AI, which is to the meet objectives/budgets of the business while also conforming to legal standards.

Customers:

Customers of the financial industry are directly affected by AI as it improves services such as customised banking, hastened loan approval, and efficient customer service. The latter makes them the driving force behind clients’ needs in the development of new forms of artificial intelligence in the financial industry.

Developers:

Working with AI algorithms and software development is assigned to developers. They aim at developing effective Artificial intelligence models for the solution of real-life issues in finance such as fraud prevention and investment management.

Ethics and Diversity Officers:

Ethics and diversity officers make sure that no unfair processes are used in Artificial Intelligence systems. They concern themselves with issues of bigotry, privacy, and data justice, thus guaranteeing that all customers are benefited by AI.

Executives:

Managers are responsible for setting the tone of AI implementation in financial organizations. , they assess the applicability of AI to develop new business strategies and to improve organisational performance and customer satisfaction.

Financial Organizations:

Banks insurance companies and investment firms are some of the biggest users of AI and computational intelligence. Organizations use AI to enhance processes, enhance performance and reduce risks in financial services.

Legal Teams:

Legal departments make sure that uses of artificial intelligence in finance do not violate local, state and global laws. They address issues of law that relate to overseeing the use of artificial intelligence in an organization including data protection, ownership and legal requirements.

Risk Management Teams:

AI is applied to help risk management teams to monitoring and managing financial risks. They use data pattern analysis to identify probable risks; they set up the financial operations’ stability and sustainability.

Governance of AI in Finance:

• This is to ensure that all the Goog AI applications adhere to the set laws, ethics, and conditions of the financial industry.
• Assigns specific responsibility for AI decisions and outputs in financial institutions.
• Ensures that the people involved have a clue on how the AI systems work in coming up with certain decision.
• Can reduce the shortcomings that come with the use of Artificial Intelligence, including infringements of data privacy, or promotion of prejudice.
• Under this type, the formation of policies that control AI creation, use, and supervision is entailed to work towards fairness.
• Needs oversight by legal departments, risk, and compliance, and top managers to track AI and respond to the risks.
• Covers the topics of always reassessing and improving AI governance frameworks in response to new issues and novelties.
• Redresses issues to do with, responsibility in decision-making, through asserting human control where necessary.
• Promotes ethical best practices to be used in the development of artificial intelligence processes that protect the customer.

Use Cases of AI in Finance:

AI is especially important in the context of finance as it helps businesses and consumers improve efficiency, security and openness, as well as encouraging new ideas. Here are the most important application areas in the field of finance.

Customer Service:

For instance, chatbots and virtual assistants are powered by Artificial Intelligence and are more efficient than human beings in terms of time response. They responded to routine questions, which leaves human beings to address more complicated questions.

Cyberattack Prevention:

AI is used in cybersecurity to scan data for threats and risks as well as prevent cyber threats. It improves security by looking for behaviors that are different from the norm and then dealing with threats immediately.

Financial Planning:

AI helps in the provision of financial forecasts as a result of evaluating a person’s financial situation. They offer individual advice concerning the spending, investing and saving which makes the financial planning easier.

Fraud Detection and Prevention:

AI is of paramount importance in identifying cases of fraud. They involve analyzing transaction data to determine unusual activities, and then marking fraudulent incidences and helping institutions avoid them in real-time.

Loan Eligibility:

AI makes the chances of loan eligibility easy to determine by sifting through huge amounts of data. This in turn facilitates the enhancement of credit risk assessment to help the financial institutions make better and faster credit risk decisions on lending.

Trading:

Trade enhances trading techniques through a detailed market analysis and the implementation of trades as results from the study depict. It can predict the market trends, and since the trader is in control, he or she can increase on the profits gained.

Benefits of AI in Finance:

The application of the concept of artificial intelligence in finance has various advantages to organizations and customers. Here are some of the ways that have kept the financial sector beneficiaries of artificial intelligence.

Frictionless 24/7 Customer Interactions:

It implies continuous customer relations for instant interactions as desired by the clients. Customers’ questions and inquiries are responded to by chatbots and virtual assistants; this reduces mean time to answer and hence increases customer satisfaction.

Reducing Repetitive Work and Errors:

AI will eliminate the human element repeatedly making errors and taking a lot of time on such tasks. This makes it easier for the employees to work on other tasks that would best be solved by human methods.

Cost Savings in Middle-Office Tasks:

AI also relieves middle-office tasks, including compliance control and risk evaluation. This results to some of the cost saving since it reduces the manual work and increases the work flow.

Aggregate Cost Savings for Banks:

By automating operations across the institution, AI presents a way of cutting operating costs in a bank’s departments. Cutting on expenses and optimizing the flow of work is possible when banks are focused on improving rationality.

Fintech: 

The potential of expanding uses of AI in financial services remains promising, and therefore the future is promising. Here are some trends configuring the future of AI in fintech.

Growing Consumer Demand for Digital Offerings:

Customers are now expecting digital services in banking and financial sector. New age platforms are AI-based and can provide tailored services to consumers, which is consistent with the trend of using the service that is more convenient and easily accessible.

Impact of Millennials and Gen Z on Banking Preferences:

Digital banking is on the rise through Millennials and Gen Z. Their propensity to use mobile apps, Artificial intelligence tools and unbroken interfaces is revolutionalising the financial services industry.

Migration to Online and Mobile Banking:

There is a faster shift to Internet and mobile banking. AI improves these platforms with services such as digital assistants, individualised financial advice, and fast transactions.

Post-Pandemic Shift to Self-Service Options:

When it comes to the nature of banking, the global pandemic in particular pushed the self-service business forward. With other less human-dependent services such as acquiring, managing, investing, and performing transactions, customers can use the AI tools that do not require face-to-face interaction.

Artificial Intelligence in Finance:

A key industry, focused in this paper, is how AI is used in financial services to boost productivity, better customer experience and generate valuable insights. The following will be of great importance while outlining AI’s impact on finance.

Overview of AI’s Role in Financial Services:

AI applies itself to numerous processes across the field of finance including trading, customer service, and risk management. It improves the processes and makes better financial services that result in better decision-making and improved operations.

Fintech and Legacy Institutions Adopting AI:

Today, AI is being implemented by both, newcomers in the form of fintech startups, and well-established financial organizations. AI makes business more efficient, cost effective, and able to find solutions to today’s problems in the financial world.

Examples of AI in Finance:

AI is revolutionizing many facets within the financial industry. Here are some of the specific ways through which AI is being applied today.

AI in Lending:

AI improves lending by processing big data to determine creditworthiness of the borrower. This results into quicker decisions and sound credit decisions hence the positive lending outcomes.

Enhancing Creditworthiness Assessment:

AI is said to better evaluate credit worthiness based on a borrower’s history, behavior and other factors that are taken into consideration. This gives a protection to the lenders while at the same time making credit available to customers in a more fair way.

Platforms like Upstart and C3.ai:

For example, Upstart and C3.ai operating as an online lender have integrated artificial intelligence into the lending process. They use various data to give improved credit scoring models to increase efficiency and inclusiveness in credit lending.

AI in Fraud Detection:

AI enhances the ability to detect fraud from transactions by constantly examining the transactions and detecting the availability of fraudulent activity. It can act early enough to mitigate fraud before it gets out of hand.

Identifying Suspicious Activity:

AI applies intelligent business practices by using machine learning to track anomalies in financial dealings. The real-time application of big data enables it to reveal fraud or suspicious activity on the basis of patterns.

IBM Watson Studio’s Role in Fraud Prevention:

IBM Watson Studio assists the financial institutions in the mitigation of fraud. Its applications are used to mine large datasets in order to identify potentially malicious actions and thus minimize fraud.

AI in Insurance:

AI is revolutionalizing insurance by handling claims and enhancing risk management. It assists insurers to deliver better targeted service and enhance internal effectiveness.

AI-Based Claims Processing:

AI facilitates claims processing by checking and approving data from the claims process. This helps to minimize the time taken in accomplishing this and also eliminates many errors, hence making it easier for customers to transact with insurance claim companies.

Lemonade’s Use of Chatbots and Generative AI:

Lemonade engages the use of chatbots and generative AI to enable the insurance process. Their AI-based system assists clients to file claims and get quotes in the shortest time and hence covering the society with insurance.

AI in Customer Service:

AI avatars and chatbots in the finance industry improve client satisfaction. They offer prompt replies, take basic questions, and offer appropriate financial solutions which enhance consumer satisfaction.

Chatbots for Banking Queries:

AI chatbots are mostly deployed in banking sector for simple inquiries like balance inquiries, transactions, payment support, etc. This makes customers to have 24/7 instant support whenever they are logged in.

AI-Driven Product Recommendations:

In banking and finance, AI helps to use customer information to recommend products to customers. This leads to enhanced customer experience regarding financial services, and more uptake of the services offered.

AI in Investing:

Investing greatly benefits from AI, especially because of the information it provides regarding the trends in the market and individual strategies. From the data analysis point of view, it assists the investors in making the right decision.

Personalized Investment Advice:

AI gives customized recommendations about investing based on the person’s needs, preferences, and tendencies on the stock market. This is helpful to investors by helping them to improve their portfolios, and investment decisions.

Robinhood and Magnifi’s AI-Powered Tools:

Many apps such as Robinhood and Magnifi make recommendations based on artificial intelligence. All these tools enable users to go through the market and come up with conclusions and decisions depending on their financial objectives.

Related Investing Topics:

It is evident that the investment in AI and related fields is a trending area and there are different possibilities. Here are some of the critical fields that organizations should focus while deciding on AI spending.

What to Invest:

They are in search of sectors of the market with potential for expansion. Machine learning and automation industries are at the forefront of attracting attention for a new economy as are fintech and cybersecurity industries.

Bank Stocks Paying Dividends:

AI is currently used in many banks and has contributed to their profitability. Hence, the investors may seek bank stocks which pay dividends, whereas firms apply AI to enhance efficiency and effectiveness and/or satisfy clients’ demands.

Best AI ETFs to Buy:

The ETFs enable investors to invest in a wide range of industries with an idea of obtaining rapid growth AI industries. Among the top AI ETFs are those that allow investors to benefit from companies that are creating AI in different fields.

Will AI Change the World of Finance?

AI is in use today to reshape the financial sector, and more of it is anticipated in the future. Here are some fundamental areas in which AI is going to drive change in finance further.

Growing Adoption of AI in Financial Services:

Globally, a growing number of financial institutions are now turning to AI technologies to seek better operational productivity, lower expenses, and improve the client experience. This is just the beginning of a new era of AI tools within the industry.

Balancing Human Judgment and AI Tools:

Despite the fact that AI technology provide more sophisticate features, human intervention is highly significant in finance. AI is most useful in the financial services industry but using it effectively means banks and other financial organizations must strike a delicate balance between using the tools and having human input in sensitive matters.

Cost-Saving Potential of AI in Finance:

AI decreases costs in the sense that it contributes towards the automation of processes, increased effectiveness of operating systems and optimization of business processes. AI remains a progressive technology that will only increase cost savings in the future throughout multiple essential segments in the financial industry.

Artificial Intelligence (AI) in Banking Market (2024-2033):

The adoption of AI in the banking sector is rapidly growing, and the predicted growth rate is phenomenal. Following are some important findings related to AI market in banking.

Market Highlights:

It forecasts that the market for AI in banking is going to grow significantly. In the year 2023, the global market was $19.90 billion and will reach $26.23 billion in the year 2024, and it is projected to reach $315.50 billion in the year 2033.

2023 Market Size:

Market value of Artificial Intelligence in banking sector was $19.90 billion in 2023. This is an increasing need by the financial service providers to integrate the use of artificial intelligence in their operations including; automation and fraud detection.

2024 Market Size – Global Market:

The market is expected to reach $26.23 billion by 2024 because more and more financial institutions continue to invest in the AI technologies to improve their services and processes.

2033 Market Size – AI Market Banking:

The anticipated market of AI in banking sector is $315.50 billion by 2033. The above growth demonstrates that AI is a long-term solution to banking by redesigning banking processes and the experience that clients have to go through.

CAGR (2024-2033) – 31.83% Estimated :

The compound annual growth rate (CAGR) of the AI market in banking for the years 2024-2033 is estimated at 31.83%. Such a rate of growth indicates the growing importance of AI in the future development of banking.

Key Takeaways:

The market of AI in banking is growing significantly fast due to the increased adoption of technologies and the overall need for digital platforms. This means that the financial institutions need to embrace the application of AI technologies.

Solutions and Services in AI:

AI solutions in banking can be provided across various services such as; fraud detection, customer service and even predictive analytics. These solutions are gradually changing the way that the banks work and how they interact with their clients.

Technology and Enterprise Size:

The current AI implementations could be observed in both traditional large banks and fintech startups, albeit on different scale. Large organisations tend to implement sophisticated AI systems, and small organisations, on the other hand, implement more specific systems.

Growth Drivers and Challenges:

AI development in banking can be attributed to digital transformation, better risk management, and operation excellence. However, the following challenges need to be met for implementation; Cybercrime risks and accuracy questions.

Digital Transformation:

AI can be seen as one of the key enablers fueling the process of digital banking. When it comes to customers as well as institutions, modernisation of bank services is well served by AI technologies.

Risk Management:

Application of AI in banking with regards to risk management has been growing recently. Big data is analyzed using the AI tools to come up with a list probable risks and information on how to prevent them, making a system more secure and facilitating decision making.

Operational Efficiency:

AI is also valuable for increasing the productivity of companies by reducing the work of employees with standard workloads and routine chores. The major outcome is that costs are minimized and services increased in efficiency for the benefit of both the bank and the customers.

Technological Advancements:

Constant development of technology in AI ensures that the improvement of banking services is a continuous process. Largely, there are new tools and solutions being developed to address market needs and this include artificial intelligence credit scoring and enhanced fraud detection systems.

Challenges:

As mentioned before, there are many advantages of AI however there are disadvantages as well: Cybercrime Risks, Accuracy Issues. Cybercrime risks and the accuracy of AI models require daily maintenance of AI-based banking solutions, so they remain safe and effective.

Regional Insights:

AI deployment is not the same across the different parts of the world, some are ahead in deploying AI while others are lagging behind.

North America:

The first movers are North America where most of the banking organizations have already integrated AI tools into their operations. These initial markets in the region are now taking pioneer market positions for other markets to emulate.

Asia Pacific:

AI Adoption is on the rise in the Asia Pacific region especially in banking. With fintech startups and traditional financial institutions opening up innovation labs and centers over the few years, the region is expected to be an active participant in the global AI banking market.

AI and Regulatory Insights:

The rules governing the use of AI are important for addressing emerging opportunities as well as risks. Various governments across the globe are weighing on frameworks for governing and handling the safety of AI in many fields such as finance.

International AI Safety Summit:

The International AI Safety Summit covers how global risks are being posed by AI and how they are being addressed. It is to address issues of concern to work toward the development of global standards for all applications of AI.

U.S. Executive Order on AI:

The U.S. has launched an executive order on AI for its proper execution and applying in our society. This order is meant to make sure that deployment of AI does not result to harm of the public or society in general.

Government and Regulator Responses:

Worldwide governments are addressing the developments of AI through policy and regulation to guarantee the safe implementation of AI across industries including the finance industry.

U.S.:

The regulation of AI is well developed in the U.S., with many endeavors to encourage appropriate application of AI. The approach adopted by the government centers on innovation, yet these developments have to protect the public interest.

U.K.:

The U.K. is also involved in the creation of the AI regulations to tackle the issues in relation to biases, non- transparency of and lack of accountability of the AI systems. The country pays great attention to ethical use of Artificial Intelligence and consumer rights.

EU:

Currently, the European Union has put in place elaborate regulations on the usage of AI to try to ensure that the technologies are utilized appropriately. Such regulations are aimed at preventing AI infringement of rights of users while at the same time encouraging advancement in AI technology.

Key Areas of Concern:

AI brings several questions that should be resolved by regulators to guarantee its correct application, especially in the financial sector. Some of the concerns are detailed below.

Data Sources:

Since AI systems depend on data for decision making, regulators are interested in data quality and its security. When it comes to dealing with prediction based AI models; it is crucial to guarantee that the foundational data set, with respect to which the predictive model is being trained upon, is accurate.

Model Risk:

The risk associated with models is the ability of Artificial Intelligence models to give incorrect or biased results. To decrease the risk, however, regulators must ensure that the AI models used are well validated.

Governance:

Good leadership is important when it comes to management of AI systems. AI solutions and applications should respect certain standards and regulations; therefore, financial institutions have to create specific governance to achieve this goal.

Consumer Protection:

One of the major foci of AI regulation is consumer protection. The protection of consumers from being exploited or further harmed by AI tools when they are using them is critical to gaining the necessary trust in the technology and its development.

Continued Improvements Necessary for Governments & Regulators Current efforts for AI regulation must advance because infringement issues continue to evolve. As stated above are some key steps regulators can take.

More Guidance – Not More Rules:

It is necessary for the regulators to give more direction on AI rather than coming up with more regulations. Specific and permeable rules and regulations are preferable as they foster creativity and prevent abuse of artificial intelligence at the same time.

A Harmonized Approach:

There is need to have a single set of regulations on the use of artificial intelligence across the globe. Through international collaboration, the use of AI technologies will be in a way that is beneficial to the global society and to individual country citizens.

Revisiting Data Protection and Cybersecurity Laws:

AI brings new threats to the proliferation of data protection and cybersecurity. As a result, regulators need to reevaluate the current legislation to adapt to new threats connected with the AI systems, consumers’ safety and their privacy.

Outlook for AI in Financial Services:

The future for AI in the financial services industry is still bright because of consumer usage and more so because of the regulatory changes that are bound to happen in the future. AI helps financial institutions to reduce cost, increase efficiency and provide better customer experience.

Continued Adoption:

It is evident that the use of AI in financial services will increase because more facilities see the value of this technology for optimizing performance. AI as a service will become more popular among banks and fintech businesses.

Regulatory Developments:

Because of this dynamics in the technological advancement of AI, the regulators will be forced to follow the developments in the technology. Continuous changes in regulations will continue to guarantee AI use in finance illegal, transparent, and efficient.

Key Companies Using AI in Finance:

There are many companies today that are at the forefront of using artificial intelligence to re-define the financial services industry. Following are some of the major actors in the field of AI in finance.

Enova:

Enova is a company that applies artificial intelligence for providing lending solutions, which means that they make a decision in no time. To minimize risk in the employees’ lending activities the company utilizes AI in the credit risk evaluation.

Workiva:

Workiva utilizes Artificial Intelligence on offer in the determination of financial reports and compliance procedures. The company assists the business in the management of financial functions and also increases efficiency through the use of AI solutions.

Trumid:

Trumid applies artificial intelligence to enhance trading and the analysis of the market. Currently, the company’s platform applies AI to enhance the provision of market intelligence and to increase trading effectiveness for the financial institutions.

AI in Finance Courses to Consider:

Introducing AI in finance is important for being relevant in the market today as the technology is already here. Shown below is a course that you can take to improve your knowledge on the application of AI in finance.

AI for Finance – Packt Publishing:

“AI for Finance”, Packt Publishing provides readers a broad overview of the subject with a focus on application of AI in finance. This course examines important AI applications such as fraud detection, predictive analysis and empowers the learner with the knowledge on how to use AI tools in the financial industry.

Conclusion:

The Future of AI in Finance:

AI is reshaping the financial landscape, offering numerous benefits from automation and cost reduction to enhancing customer service and improving risk management. As AI continues to evolve, its role in financial services will only grow, driving innovation and efficiency across the industry. Financial institutions must balance AI adoption with effective regulation to ensure ethical use and consumer protection.

What are your thoughts on the growing role of AI in finance? Feel free to share your insights in the comments!