Throughout history, radical new ideas have always been catalysts for profound change. From a technological perspective, the events that have changed the world the most are the invention of innovative new solutions.
In the early 1980s, the emergence of the Internet and the resulting era of big data completely changed the landscape of global business. Today, we seem to be living through another pivotal moment, with the rapid acceleration of artificial intelligence development driving seismic change across industries.
Looking back, the advent of digitized services ushered in the era of big data, enabling companies to collect information about consumer preferences, implement analytics, and use rich data-driven insights to transform their business approaches. . Since then, advancements in technologies such as various forms of AI, rule-based systems, low-code tools, and machine learning algorithms have continued to be added to the technology stack, improving the ability of these organizations to maintain a competitive advantage.
Additionally, companies have increased access to data and leveraged the power of business process optimization tools (BPOs) to transform the way they work, create operational efficiencies, and improve the overall employee and customer experience.
Despite all these advances, we still lack the ability to communicate with machines in a “human way.” The emergence of generative AI, the next big thing in the business world, may just be the tool that gets us there.
Generative AI: More than just a buzzword
We may be able to publish the first major breakthrough in deep learning by:
All you need is attention But it wasn’t until late 2021 that generative AI, a type of automated learning technology that can generate new written, visual, and audio content from existing data, changed the game.
Using deep learning techniques, neural networks, and other advanced algorithms, we have demonstrated an incredible ability to analyze and generate new information that is applicable and scalable to a variety of business use cases. Companies in fields as diverse as finance, healthcare, retail, and more have integrated generative AI into their workflows to create cutting-edge solutions and disruptive value propositions.
Generative AI takes assistive technologies to a new level by allowing machines to “chat with data”, delivering powerful capabilities to both technical and non-technical users and reducing application development time across organizations. In the spirit of going beyond jargon, we set out to identify and describe a variety of use cases from which processes and operations in the banking industry can benefit.
Use cases in banking
As you would expect, there are numerous use cases that apply directly to the banking industry. At the sales and customer service level, robotic process automation (RPA) can embrace new capabilities through a more personalized approach to natural language. On the operational side, decision automation and a newly upgraded BPO suite help streamline more complex tasks and increase productivity and competitiveness. Generative AI also fills the talent gap by supporting legacy code modernization and development to compensate for the lack of highly technical roles in IT.
Unlock synthetic data
Customer data, such as financial information, is so sensitive that banks and financial institutions must protect it at all costs to prevent data breaches and comply with GDPR and other regulations.
At the same time, this data is also important for building AI models that underpin processes such as virtual financial advisors or portfolio optimization tools. These algorithms “feed” large amounts of information to identify statistical patterns and learn and replicate important properties.
By generating synthetic data – data that most closely mimics accurate information – without using sensitive information such as bank customer names or account numbers, companies can provide the volume and variety of data needed to train and fine-tune these models. there is. Definition of BI, AI rules.
For example, Generative AI’s powerful neural networks can replicate and tabulate information such as lists of past financial transactions, allowing behavioral analytics to design customized products and services for each bank customer.
Breakers for generative AI
AI models, which are statistical tools trained on massive amounts of data, rely heavily on the quality of the information provided. When given incorrect input, it can process untrue details, learn from them, hallucinate, and output inaccurate answers. To prevent this, AI must incorporate principles, regulations, and standards that encompass an ethical AI strategy.
Of course, generative AI has the power to bring about change, but it is still not the human brain. Because they lack reasoning skills, their decision-making must always be supervised. In conclusion, the responsibility still lies with humans. Technology can be a useful co-pilot, but humans must always go with the flow.
Our Vision for the Future
The availability of pre-trained models makes the Generative AI journey more accessible. Nonetheless, as technology advances rapidly, organizations must define a strategic approach to effectively integrate AI into their daily operations.
Embedded within the Digital Technologies department, we have recently opened a brand new AI hub in Valencia. The main goal is to develop AI by design. This means:
- Building the right ecosystem consisting of partners, users, and platforms
- Drive AI adoption with a clear value proposition
- Adapt technology to your organization’s culture and values
- Transform existing production models to implement new initiatives.
To sum up our vision in one sentence: We believe that the real challenge of AI is not about the technology itself, but about articulating the right deployment strategy, organization, and governance.