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How is AI driving continuous innovation in finance?
The finance industry is undergoing a transformation that involves AI, data, and deep learning. This blog will give you an overview of what it is all about. And what AI holds in the future for the banking and financial industry.
The influence of AI is constantly increasing in the world around us; no industry is left untouched by it. Whether in the military, education, forensic investigation, scientific research, entertainment-based, human resource, tourism, healthcare, or the financial sector, we can find the impact of AI everywhere today. It is helping these business models transform and generate more consumers by enhancing the product offerings.
And nowadays, the concept of AI in banking and finance is especially given more attention due to its tremendous untapped potential and countless benefits. Different companies and providers worldwide are rapidly deploying AI in the financial sector to make the best out of this idea and use its numerous possibilities to make their service transparent, reliable, orderly, fair, and easy to access for every person.
So, in this blog, we will highlight AI’s effect on the financial industry and expand more on how it is a driving force of innovation in this direction.
Let’s get started.
Introduction of AI in Banking & Finance
AI isn’t only transforming different business models. It is also reforming how corporations and enterprises generate data and utilize these insights to promote and expand their outreach and brand image. And by using AI in banking and finance, we can now improve the trading market, reduce friction, generate efficiencies, and boost the growth of various fields like credit and blockchain-based finance, commercial units, and crypto-based industries.
AI combined with data management and ML can breathe life into the more or less constant and stagnant nature of the financial industry. It can amplify the results and pave the way for broadening the implementation of numerous innovative ideas and how we can use them to reduce the potential risks associated with the world of finance.
We also need to be on the lookout as AI in banking and finance can also possibly amplify these already existing risks. It could also introduce a few newer ones and make the work in this section more challenging and filled with perils and virtual data hacking.
But that doesn’t stop us from exploring the countless opportunities packed in this field. We, for now, know the implementation of AI in the financial sector is helping the world grow substantially due to the availability of abundant data and the constantly increasing affordability of computers and supercomputers. And there’s no doubt this trend will eventually become a bigger hit in the coming years, especially from 2024 to 2030. 2022 has already begun to witness the transformation brought by AI in the banking and finance industry.
So, to further understand how AI is revolutionizing banking and finance and the various aspects of this technology in detail, let us go through the different elements comprising this globally reforming enactment.
What are AI and ML?
AI or Artificial Intelligence refers to the compilation and combination of computers and machine systems programmed to predominantly mimic the actions and perform the activities of a human being. The central ambition and theme of AI are to increase the capabilities of the human working skill and make it a tremendously powerful, beneficial, and transforming asset on a global level.
AI has a subset known as ML or Machine Learning. This field is responsible for creating and developing systems and computer connections that can learn from the different models by extracting and collecting data for their information consumption. ML also focuses on improving the disabilities existent in the channels and models it works on without relying on the interference and accessibility of human skill and power, that is, compiling programs and coding.
These days, AI is constantly revising the needs and conditions to set up a successful enterprise in the financial sector. Plenty of excellent opportunities are available in the world of finance and money management through the implementation of AI and ML. Advantages like leveraging artificial intelligence to define customer investments related to their personal goals and using machine learning to enhance operational efficiency are some of the mentioned ones from this endless list.
And given their versatility in working with different data models, be it related to statistics, ecology, technical research, chemical half-lives, or business and finance, it is no doubt that AI can become a well-established partner in the financial industry. We can utilize the various benefits of AI in banking and finance by driving scalability, renovating business models, and reshaping workforces.
ERP Finance
Finance refers to the actions and examinations to study the movement, investments, and management of money and currency. And as we go deeper into the prime concept of money management in finance, terms like lending, borrowing, forecasting, budgeting, saving, liabilities, assets, and investing become ubiquitous.
In businesses and the commercial trading industry, finance is the supporting nervous system for every organization. It is the backbone for handling the crucial economic aspects necessary to keep the business and trade running successfully. These include purchasing raw materials and assets, paying for the supplies and employees, and mapping the future business perspectives and expansion plans.
What is ERP in finance?
ERP (Enterprise Resource Planning) is the perfect example of how AI in banking and finance can prove to be a valuable feature in the financial department. It is the name of the software used by organizations to aid in their financial management. It helps them keep track of their accounting, projects, procurement processes, and various other points throughout the enterprise.
Back-office functions and operations like accounting, supply chain management, procurement, analytics, risk management, enterprise performance management (EPM), financials, etc., can be well handled by ERP.
But, for many departments and companies in the financial sector and IT industries, ERP systems can be synonymous with costly, large, and time-consuming hardware deployment and infrastructure investments. But if we combine the technology of cloud computing and SaaS (Software-as-a-Service), we can change the forefront of the business and how they think and work.
Renovating the ERP with cloud and advanced AI features will allow corporations and organizations constant access to innovative ideas for their work and receive a faster return rate on their assets. It will build their financial investment and management by simplifying their technological requirements and paving a new path with the introduction of AI in banking and finance.
Data-Driven Banking: How Is Data Changing The Banking Landscape?
This blog post will briefly introduce how banks and financial institutions can transform customer centricity into a competitive advantage through data-driven banking. What are the different data-driven banking use cases? And lastly, how to get started.
AI and financial activities
One of the trickiest obstacles companies face while adapting AI in banking and finance is accessing data and data quality. The distribution of data occurs in RAM on multiple servers. And since data closely resembles an application, we can use AI in financial activities to get fast ROI, factual information in real-time, and low latent rates for data quality and generation. The performance stays consistent and maintains its balance even during the events of peak activity or traffic spikes.
AI and financial activities also face the challenge of handling talent requirements. The wisdom and skills needed to ace the market are usually poles apart. So, the organizations and firms must prioritize which assets and abilities they want to go for and harvest for their business model. And once they decide, it is up to them how they will implement the same in their workplace to get an optimum output in the coming years.
The concept of provoking empathy through data in finance
AI is also very beneficial in stirring empathy amongst the people in your financial business. In today’s world, when digitization and virtual marketing are spreading like wildfire due to their tremendous advantages, many business managers are swiftly adapting to machine learning and data interpretation to predict the dynamic nature of the market. And by using AI in banking in finance to provide empathy and compassion to your customers, you can inspire and motivate them to prefer your services over your competitors.
But it is easier said than done. The data you gather from email marketing, mobile messaging, channel advertisements, communication on cross platforms, etc., are bulky. And you need to accurately manage them to pinpoint the objective and demand of the users in the market that is frequently changing. The numerous challenges in this process can be a terrible limitation if you don’t adopt an efficient method to tackle them. Luckily AI solves each one of them. Here’s how:
- You can smoothly manage tons of consumer data in a short period.
- It is ideal for recording the constant change in data and reflects the reality of the financial industry’s dynamics.
- You can build a real-time view of how a user responds to your service and what measures you can take to improve this result.
AI and empathetic marketing
Taking action and execution require many inputs, tedious calculations, and high maintenance at the industrial level in the financial business or company. Maintaining the relevance of the engaging factors and updating your system with the behavioral data of your clients and subscribed patrons require you to uphold a holistic opinion of the commercial field. And nothing but AI could help you here. Tasks such as listening to every customer and resolving their queries, catering to the needs of those followers who have a negative response against your brand, and creating personalized and user-friendly experiences for everyone would be nearly unattainable without AI.
Through AI in baking and finance and ML software and tools, you can find those users from the audience who enjoy your services and have given positive reviews for your products. You can further develop the marketer-consumer bond with them and personalize their experience by paying attention to their demands and opinions. Nurturing the appraisals of your clients and working on fixing the drawbacks makes you a reliable entrepreneur in their eyes and helps you gain moral support in exchange for the empathy you provide them. It reduces stiffness in your company and opens a new path for generating revenue.
Advantages of finance AI
Here are some of the benefits of using AI in baking and finance:
1) Accuracy
- Lessening of risk-related events
- Reduction of faults in automated tasks
- Enhanced forecast, planning, and accuracy of model programming and charting
2) Productivity
- Quicker insights and data values
- Less time for developing narratives and factual reports
- Enhanced employee productivity
- Reduction in time consumption to create and audit financial statements
- Faster completion of monthly financial close
3) Business value
- Creates a healthy competitive atmosphere for enhanced working
- Strengthened workforce through thoughtful planning such as addressing talent gaps, signifying salary costs, etc.
- A better and profound comprehension of the business’s performance levels and how to improve it
- More profitability through recognizing the higher and lower levels of profit and success areas within the company
How is AI driving continuous innovation in finance?
Here are some points through which AI is bringing innovation in the domain of the finance and banking industry:
- AI is improving the customer service department in the banking sector. AI-powered chatbots are successfully offering clients self-help solutions and reducing delays in service provision for each customer. It also diminishes the pressure to work tirelessly in the call centers for long hours.
- AI is improving productivity in the financial and commerce sector. Various robotic computers and devices are upscaling the power levels of work and simultaneously reducing costs. It is helping the companies cut down their budget and invest in harvesting new and helpful ideas to get reviews from the users and implement them quickly.
- AI is offering risk management in the financial sector. Managing structured and unstructured data is very challenging for humans. But through AI-enhanced tools, we have come to analyze troublesome and historic cases without breaking a sweat and making accurate forecasts and predictions regarding different situations.
- AI in banking and finance is preventing the spread of fraud. Credit card shams through online transactions have become easy to trace and identify. As a result, we can rely on the necessary authorities to take immediate actions in this direction and protect our data and money from getting stolen.
- AI is boosting trading through Intelligent Trading Systems. They can monitor structured and unstructured data and process results faster than ever. Stock predictions and cryptocurrency forecasts are more accurate now. And it has also become more straightforward for the government to regulate benefiting schemes to aid in the development of trading in the financial sector from the audience’s perspective.
- AI is predicting credit worthiness of borrowers. With automated AI/ML capability credit underwriting solution, aimed at pre-qualifying leads and making automated credit decisions for banks and financial institutions can help lending businesses to reduce the overall credit cost by improving the quality of loan disbursals.
Examples of AI in banking and finance
As AI is sweeping the market with its fresh innovations and technical advancements, brands and companies worldwide are implementing AI in their service and products to keep up with the constant changes and latest trends. Various AI-deployed machines and AI-driven innovations are becoming the need of the hour for entrepreneurs and business leaders who want to stay on the top of the financial industry and get the best out of this project.
Some latest examples of finance AI are:
- Organizations are using the latest cloud tech to automate the manual ERP system and built-in AI tools to speed up the process. These renovated systems can detect frauds or reconcile accounts using automated data entry, like supplier details, materials bought, estimated cost, etc. They can also scan physical invoices and trace the crucial information and detail in no time.
- AI in banking and finance has helped businesses utilize automatic financial close processes to transform employee activity from manual to automated. Through unbiased forecasting and scenario modeling, data actions like collection, analysis, strategy, and execution have become ten times easier, less costly, more accurate, and faster.
- Many companies also rely on AI-guided digital assistants to make their process of collecting information and doing work more straightforward and transparent. It has made the entire process of remembering complex query language for interacting with the ERP system less painstaking.
Risks of not including AI in finance
According to a report published by Oracle Monkey and Machines, around 91 percent of the Gen Z employee and 83 percent of the Millenials are trustful of AI-enhanced tech and robots for handling and maintaining their finances. And, at least 87 percent of business entrepreneurs and leaders think that not investing in finance AI is risky for companies and brands worldwide.
These organizations might eventually face various other issues and obstacles such as:
| POTENTIAL RISKS | INVOLVED PERCENTAGE ESTIMATION |
| Stressed workers and an unhealthy workplace | 36 percent |
| Inaccurate reporting and data summation | 35 to 36 percent |
| The decline in staff members’ productivity | 35 percent |
| Lagging behind competitors in the market | 44 percent |
| Becoming less appealing to the next generation of users | 17 to 21 percent |
How to get started?
Investing in finance-based AI can be intimidating and somewhat confusing in the beginning. It can significantly impact your business career and the overall productivity rate of your company or firm. To have a good headstart and maintain a firm foothold in this industry, remember to consider these facts before beginning:
1. The best machine learning devices
There are plenty of ML tools available in the market to implement AI in finance. So, you should choose the best use-case and well-defined features to expand your marketing and business further in this field. ML tasks having 43% approval, 39% forecasting and budgeting, 38% compliance, and 38% reporting are ideal picks.
2. The right skills in AI
These are some of the crucial and higher-level skills you must look into before selecting an AI for financing:
- It should handle most of the manual accounting tasks.
- It should be able to provide risk management, business strategy, and data-based communication.
- It should quickly spot anomalies.
- It should know data interpretation, interact with stakeholders, and feature storytelling elements.
3. Custom-built AI apps and the AI-built ERP systems
If you want to invest in AI in banking and finance, you can select the custom-built AI apps or the AI-built ERP systems to do the work for you. Both come with pros and cons and are beneficial at their level.
If you have a team of data scientists and researchers who are well familiar with the concepts and designing of AI, you can always choose to go with custom-built AI apps and design them for yourself from scratch, depending on your requirements.
But if you are looking for a more readymade system already having cloud implementations, AI-built ERP systems will be the best option. Besides, if an error occurs, it will be the cloud service provider’s responsibility and not you in this situation.
Emerging risks of using AI in finance
Everything comes with its set of flaws and drawbacks. The same is for AI in banking and finance. As the spectrum for including finance AI is rising tremendously, numerous possible risks and challenges are also emerging in this direction. The challenges could contain a wide array of elements, such as robustness of AI models, accountability in AI systems, possible risks of mitigation tools, regulatory deliberations, explaining ability, job and position stakes, etc.
These emerging setbacks need quick action and should be identified and given further consideration by policymakers. So, in this section, we will focus on some of these disadvantages and try to understand their causes.
1. Data management and confidentiality
Data comprises the building block of any AI-empowered application. But its unsolicited use can cause various non-financial risks to the companies and business leaders. These challenges could relate to potential data piracy, inappropriate access and misuse of private data, unfairness behind using AI-powered tools, and many more.
2. The bias of algorithms and discrimination in AI
If utilized adequately, AI-based algorithms have the potential to diminish discrimination and any source of bias related to human resources in the financial industry. But if the ML models get misused for illicitly trafficking data, it can lead to discrimination in the algorithm. The models perpetuating bias will generate more biased codes and models, thus further corrupting the system and causing an eventual breakdown.
3. Governance of AI system’s accountability
The governance and transparent accountability of AI-based systems are indispensable for AI in banking and finance. But if questions related to reliability and controlling of these models and methods start erupting, the functioning and results can go down in no time. Thus, the conclusions we take regarding the data collected and generated are crucial and shouldn’t depend only on the existing governance and oversight arrangements.
It is very much possible that an AI can behave contrary to the demands of the consumers in the market. And if left unchecked, it could potentially cause damage to the financial sector on a large scale. Therefore the final accountability of the AI systems is also an equally vital aspect in this field.
The future of data, commerce, and AI in banking and finance
Experts and specialists have said in several articles and magazines that data and AI will no sooner change the world. Nothing will remain unaffected by their touch, and things will come together to become a part of this technological trend. But how does it apply to empathy in financial marketing?
If you look closely, you will understand how third-party systems are slowly merging into zero-party systems and helping the manufacturers establish a direct and secure link with the receivers. It will also boost the demand for the first-party system where we rely on the transparent data collected from the consumers on a large scale.
By gradually improving the significance of zero and first-party data systems, websites and companies are now establishing transparency between the customer and the service provider. And AI technology is also aiding significantly in this procedure.
By ensuring that the viewers get top-notch service, communicating channels that understand the audience, and a user-friendly site interface, the request for transparent and dedicated services is increasing steadily in the consumer domain. This boost in demand for clarity in the financial sector is one of the prime factors that has helped AI establish empathy in marketing and allow entrepreneurs and leading investors to treat their clients with compassion and familiarity.
Wrapping Up
AI in banking and finance has the authority to deliver terrific results in the financial service industry. And by combining AI and empathy, we can generate significant results to understand and connect with the audience and provide satisfying solutions. It can also make the working of the financial firms more effortless, cost-efficient, and modernized by reducing their drawbacks and dishing out new opportunities to implement innovative concepts in this domain.
But it can also amplify the existing issues and give ease of access to various pirating groups or cause the creation of a terrible flaw in the well-established AI system. Therefore, what matters here are the policy considerations that can help uphold the order and establishment of financial AI and prevent its misuse at every possible step.
And while AI in finance could create various problems for us in the future, if you consider the impossible amount of excellent possibilities and prospects available, these drawbacks will appear insignificant. We must focus on the risk only to work our way to keep the system nearly perfect and in favor of the consumers, and not as a way to stop ourselves from growing and exploring this infinitely robust and influential area.
Lastly, if you are looking for help with implementing any of the foresaid use cases using artificial intelligence, machine learning, or deep learning models, we can help. Whatever your needs are, we can help you with our data science and analytics services and become your reliable partner. Want to learn more? Talk to one of our experts.
I write about fintech, data, and everything around it | Assistant Marketing Manager @ Zuci Systems.
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