Artificial intelligence-led changes in the financial industry will create the banks of the future


liu, tempo Date: 2021-08-12 09:32:40 From:ozmca.com
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Looking back at history, you will see that the financial industry is the most difficult to achieve change. But inevitably, big banks and startups are still making huge breakthroughs in finance, not, I think, because of any particular technology they use, but because of their inherent cultural differences, diverse structural rigidity and other cost-effective business models.

 

Financial innovation: too much empty talk, too little actual action

 

In other words, banks are not innovating either because they are too big to adapt quickly and follow external incentives, or because they do not know how (or want) to really change. This is true not only in the financial industry, but also in academia, where there were no breakthroughs in financial innovation until the mid-1990s. In fact, in a small survey of the literature, over 600 different articles and books are cited, but none of them are related to financial innovation. Certainly, things have changed in the last five years, but I would argue that the change has been reactive rather than voluntarily driven from the banking industry.

 

As a result, financial innovations seem to be typically introduced from outside rather than generated from within, and tend to be more product innovations than process innovations. Given the new technological paradigm (which is reinforcing the inherently strong causal relationship between innovation and growth), it seems natural to wonder if a better model of innovation could be imported by a different industry.

 

I found a very special and interesting example of an industry that must “innovate to survive” rather than “innovate to grow”: the biopharmaceutical industry.

 

Innovation shift: the biopharmaceutical industry

 

The biopharmaceutical industry is not a single industry, but includes two distinct technology sectors: the biotechnology sector, consisting of small companies that have driven the research and discovery phase; and pharmaceutical companies, the large companies that have become large public and marketing companies over the last century.

 

So, part of it is pure (high-risk) innovation, and part of it is pure commercialization skills …… It’s all stuff we’ve seen before, isn’t it? The biopharmaceutical industry and the financial industry form a clear dichotomy

 

The biopharmaceutical industry is characterized by the fact that the risk exists mainly in the initial development process, not in the marketing phase. The problem is not meeting customer needs or finding a market for your product, but developing that drug molecule in the first place. The likelihood of success is very low, the timeline is long (10-15 years), and the 20-year patent rights are only a short-lived advantage. What’s more, with only about two-thirds of drugs offsetting development costs and most companies losing money, and the top 3% of companies making almost 80% of the industry’s profits, it’s a tough business.

 

The biopharmaceutical industry is no longer just a labor-intensive industry, but one that requires significant capital investment. Innovation is not an add-on, it is the cornerstone of a company’s survival and growth. That’s why they must identify a range of different approaches to their growth – innovation: R&D, competitive partnering programs, venture capital, joint venture creation, acquisition deals, limited partnership agreements, etc.

 

Artificial intelligence

 

So far, my goal should be clear: the financial industry does not feel as strongly the need to innovate as the biopharmaceutical industry does, and it does not try and push to create new models to get maximum benefit.

 

Introducing artificial intelligence, your personal financial disruptor

 

Now you may still be thinking “innovation is great, but finance and biopharma are two very different industries”, so why should I insist on bringing in innovative models from other industries? Well, that’s the problem: I don’t think they’re different.

 

And the reason they are becoming more similar is precisely because of artificial intelligence. Artificial intelligence is injecting a powerful innovation force into the financial industry, and it has a development cycle and characteristics similar to what is happening in the biopharmaceutical industry: it takes a long time to be created, implemented and deployed correctly; it is highly technical and requires highly specialized talent; it is highly uncertain because you need to experiment a lot before you find a viable solution, and AI is putting tremendous pressure on the financial industry to innovate.

 

But AI is also bringing a whole new level of speed and credibility to the financial industry, reducing similar errors in the biopharmaceutical industry. It’s very easy if your algorithm points out a problem product or the wrong book being recommended. If your system misinterprets some signal in the market or makes a mistake in developing a drug, you can lose millions of dollars in seconds, or even your life.

 

Thus, it not only extends problems that are essentially financial, such as regulation or accountability, but also introduces new problems, such as biased data or lack of transparency (especially in consumer applications).

 

Finally, AI raises a question about “build vs. buy” that is even bigger than the biopharmaceutical industry was in the 1990s, culminating in the current biotech-pharma dichotomy (in case you are wondering, this choice focuses on your data capacity, the scalability of your team and program, and the uniqueness of your program in relation to your competitors). uniqueness of the project in relation to competitors – do you have enough data to train an ANI? Is the size of your team/project sufficient? Is your ANI unique? (Are your peers doing something about it?)

 

Artificial intelligence is revolutionizing innovation in an industry that is centuries old. That’s why I think it’s important for the financial services industry to introduce AI – not so much for the specific innovations or products it introduces, because it’s revolutionizing a centuries-old industry innovation process.

 

Functional breakdown of AI in FinTech

 

Artificial intelligence is using structured and unstructured data in financial services to improve customer experience and customer engagement by doing so to identify outliers and anomalies, increase revenue, reduce costs, find predictable patterns, and improve the reliability of predictions …… But is this true in other industries as well? The answer to this is obvious, so what is so special about AI in the financial services industry?

 

First of all, the financial industry is an industry that requires a lot of data. You might think that this data is concentrated in the hands of large financial institutions, but much of it is publicly available, and with the new EU Payments Directive, larger databases can be used by smaller firms. Artificial intelligence is easy to develop and apply because the barriers to entry are relatively low compared to other industries.

 

Second, many of the underlying processes can be automated relatively easily, while many others can be improved by step-by-step computing or speed. Historically, AI has been one of the industries most in need of such innovation, with fierce competition and always looking for new sources of investment. To summarize: the marginal impact of AI is greater than in other areas.

 

Third, the intergenerational transfer of wealth makes this area truly “fertile ground” for AI development. AI requires a lot of new data and, most importantly, some feedback for improvement, and the post-00s are not only happy to use AI and provide feedback, but they clearly care less about privacy and giving away their data.

 

Of course, AI in finance faces a specific set of challenges that hinder the smooth and rapid implementation of smart finance: legacy systems that do not communicate with each other; data silos; poor data quality control; lack of expertise; lack of management vision; and lack of a cultural mindset to adopt this technology.

 

Thus, all that is missing is an overview of the AI fintech landscape. There are also many maps and classifications of AI fintech startups here, so I am not introducing anything new here, but just showing you my personal classification of

 

Financial wellness: this category of applications is designed to make the end customer’s life better and more convenient and also includes personalized financial services; credit scoring; automated financial advisors and planners that help users make financial decisions (robo – advisors, virtual assistants and chatbots; smart wallets that can be used in different ways depending on the user’s habits and needs, and guide users in different ways. Module Chain: I believe that given the importance of this tool, it should have a separate category, regardless of the specific application (which could be payments, compliance, transactions, etc.).

 

Financial Security: This can be divided into identification (payment security and physical identification – biometrics and KYC) and detection (tracking fraud and unusual financial behavior – AML and fraud detection).

 

Funds Transfer: This category includes payments, p2p lending and debt collection.

 

Capital Markets: This is a large segment, and I tend to divide it into five main modules.

 

1) Trading (trading or trading platforms).

 

2) Self-help funds (crowdfunding funds or house deals).

 

3) Market intelligence (information extraction or insight generation).

 

4) Alternative data (most alternative data applications are in the capital markets, not in the broader financial sector, so it makes sense to put it here).

 

5) Risk Management (in most cases, this part of the startup also involves other modules).

 

From the beginning of the article, I have been emphasizing that AI is making the financial services sector more and more similar to biopharmaceuticals, and that the financial sector might be able to learn something from innovations in other industries. The reality is that the financial industry still has some difficulties and challenges to overcome.

 

One of the biggest differences I see so far is the impact of AI on the physical product market, where AI is making the industry more digital than ever before. Its ultimate goal is to create the bank of the future: no branches, no credit cards, no fraud. A banking platform with modular components that will improve our financial literacy and eliminate the need to buy physical products. It’s definitely a new world to aspire to, and I can’t wait.

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