Fintech is a quickly changing environment, adapting to new technologies and innovations. So, let’s take a look at a couple of problem-solving technologies that are seeing a rise in applications at the moment.
Quantum Solutions
One of the key issues facing the future of fintech is quantum computing. With such large computing power, quantum computers could one day crack the results of traditional cryptography techniques. These techniques typically rely on random number generation (RNG), which has been utilized in the online casino industry for decades due to its essential generation of luck, chance, and fairness. For example, when players try their hand at games like Hypernova Megaways, the results of each spin will be entirely down to chance.
This is because the same technology used to create cryptographic keys ensures that the symbols fall onto the reels in an entirely random order. Thus, the results of the game are unpredictable, making the gameplay more immersive.
Whilst in this example the RNGs are typically software-based, fintech applications have begun turning their heads towards quantum-based RNGs (QRNGs). Krown Technologies Ltd and Quantum eMotion Corp signed a memorandum of understanding in December 2024 in an attempt to use QRNG technology in blockchain ecosystems to work towards a post-quantum cryptography (PQC) solution. The partnership between the two companies aims to lay the groundwork for a new standard of security for blockchain infrastructure and cryptocurrency wallets, which could further influence other financial sectors in due course.
Elsewhere, multinational banking and financial services corporation HSBC has begun using quantum-secure technology for the trading of their tokenized gold through distributed ledger technology (DLT). Using PQC algorithms and QRNG technology, the bank, in partnership with Quantinuum, was able to convert their gold tokens into ERC-20 fungible tokens for trading. This showcases how such technology can be used to protect physical and digital financial assets alike, both through tokenization, PQC methods, and the security of data transmission.
Artificial Intelligence
According to NVIDIA’s fourth annual State of AI in Financial Services Report, last year 91% of businesses providing financial services are either assessing whether to use AI or are already using it. Further to this, 97% planned to invest more in AI in the near future. This is because AI (when trained and used responsibly) could potentially be used to solve a number of problems in the financial industry.
For example, some financial processes are incredibly knowledge-intensive. If done manually, this can take a long time – and, even when using software it may require switching between applications. Here, brands like Contextual AI are using retrieval-augmented generation (RAG) to bring extraction, retrieval, reranking, and generation into one system. HSBC – always on the front foot, it seems – is reportedly planning to use this technology to retrieve and synthesize market outlooks, operational documents, and even financial news.
Whilst randomness in some cases, like above, can be great, sometimes it can be a pain. Disorder, randomness, and uncertainty can greatly affect workflows in the financial sector. To clean up text in statements, receipts, and other forms of unstructured data, Ntropy has created an application programming interface (API) that standardizes financial data at an estimated 10,000 times lower cost than traditional processes.
And there you have it – just a couple of problem-solving technologies that are expected to be at the forefront of fintech development in 2025. All that’s left to do is see how these technologies continue to emerge and adapt.
