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Understanding the future of finance: Strategic planning assumptions

Understanding the future of finance: Strategic planning assumptions

By Walter Paliska, VP of Marketing of dotData 

Predicting the future may seem like a difficult task, especially today in a world where innovation is disrupting industries at an incredible pace, as we have seen with generative AI. However, business leaders must engage in the exercise of imagining the future to stay ahead of the competition. Making strategic planning assumptions and testing their services against possible future scenarios is the way forward. 

The future of finance is already being shaped by embedded finance, AI, data and analytics business intelligence (BI), and other technologies like quantum computing

A recent survey conducted by The Economist Intelligence Unit reveals the importance of understanding new technologies. The survey found that 77% of bankers believe that having the ability to unlock the value of AI will be the difference between business success or failure. Let’s look at the top technologies and how financial companies can begin building the future today. 

Automation of data processes: Humans and machines working together 

During the recent Gartner 2023 Data and Analytics Summit, in Australia, Kurt Schlegel, Research Vice President at Gartner, spoke about the transformation of data and analytics teams. Schlegel warned leaders to expect data and analytics BI manual processes to be fully automated in the near future. 

The majority of the financial data processes are done manually. However, data integration, report generation, data analysis, BI presentations, data mining, and data products, will soon be done by machines driven by AI and machine learning.   

Leading decision-makers must shift today to embrace automation. The change not only implies technology buy-in but a shift in talent and teams. Data teams will likely shrink or at least not grow as rapidly as they have in the past, and data scientists and data engineers will soon take full advantage of AI and automation systems. Companies are already recruiting talented AI experts from different fields like philosophy, linguistics, design, and other backgrounds to lead the automation transformation.  

Super apps, personalization, meta-data, and consumers as product developers 

BI, and its automation, combined with the use of metadata, which allows companies to gain key insights into their customers, is also gaining momentum and giving way to a new era of financial services. 

Leading institutions are converging upon the market by launching Super Apps to offer a wide range of services, expand their portfolio, and attract new clients. Super Apps are becoming a one-stop-shop solution where clients can do everything, from banking to insurance, to credits and mortgages, market investment, real estate, and cryptocurrencies. 

Super Apps also thrive on metadata which is driving personalization to unseen levels. Just as Netflix knows what a viewer likes and dislikes, when they hit pause, and what their new movie preferences are, similarly, financial organizations are beginning to understand how to use metadata to deliver hyper-personalized content for users. 

While many see the use of metadata as an organic, natural evolutionary step for financial services providers, the reality is it is much more. Metadata will create a monumental shift in traditional business models. The traditional consumer-financial service provider relationship will evolve as consumers intuitively tailor the products they need thanks to the invisible assistance of AI systems capable of analyzing customer metadata and generating new portfolios. 

Trust, governance and security

Fraud levels in the financial sector are so significant and widespread that they affect our global GDP by the trillions of dollars. Companies often think of fraud as the inevitable threat and loss. However, new machine learning models and AI systems are already being used to combat fraud. 

By automating the fraud detection process, companies can reduce human error, increase fraud detection capabilities, and free up valuable time for their expert fraud analysts to focus on more vanguard issues. Machine learning and AI anti-fraud models are already offered by all top cloud vendors, from Google Cloud Platform to Amazon Web Services or Microsoft Azure. These technologies can detect fraud in real-time using historical data and rule-based algorithms. 

On the other hand, while generative AI will bring many benefits to the industry, it is also a double-edged sword. Generative AI tools sold on the dark web are being used to generate deep fake videos, images, and audio, mimic official sites, code malware, and write incredibly realistic phishing emails and messages. Banks and fintech organizations will have to deploy AI detection solutions to respond to this new cybercriminal technique which is only expected to rise. 

Quantum computing in the cloud and advanced AI

Quantum computing, a technology, which utilizes the laws of quantum mechanics to go beyond classic computer computational powers, is moving from experimental to production stages. IBM Quantum, Microsoft Azure Quantum, and Google Quantum AI capitalize on the technology and open it up to any financial organization willing to use it. Similarly, the use of advanced AI in the cloud is picking up speed. 

By deploying advanced AI and quantum computing in the cloud, companies no longer face the initial expensive costs associated with building these systems in-house. And as the technology becomes more globally available and easier to deploy, dramatic changes in the financial sector are expected. 

The most attractive feature of quantum computing and advanced AI for finance is undoubtedly the speed, reliability, and accuracy of calculations. In finance, better and faster calculations  translate into profits and less risks. Quantum computing and advanced AI can be used for portfolio creation, profit margin analysis, asset price fluctuation estimations, stock markets and derivatives trading, and credit risk assessment, among other things.  

Generative AI is also expected to become a main player of the customer-facing departments. From sales bots to intelligent multi-channel support systems, once again, the relationship between customers and financial organizations will be transformed into an AI-human relationship. 

Why leaders must look into the future 

Companies must understand that having an eye set on the next quarter, 6, 12, or 18 months or beyond, helps develop better services and products. By testing products against different possible future scenarios, companies can have a more empirical and forward-looking approach and draft better strategic business plans. 

Understanding the technology and concepts that are trending today, and will lead the way in the future,fosters a culture of innovation, builds talent and values better, and strengthens confidence. The uncertainty of the future is, without a doubt, something everyone has to deal with. However, strategic planning assumptions are vital for financial organizations to identify new business opportunities, ensure integrity and continuity, enhance values and missions, and prevent threats, disruptions, and risks, while innovating to make a responsible impact. 


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