As we start 2023, nearly all established economies have entered a period of double-digit inflation and rising interest rates combined with unusually high levels of employment. This is putting a unique pressure on your profitability. What’s the answer?

As ever, innovation is coming to the rescue. Technologies like AI and machine learning are evolving to transform the way in which information is processed and utilized. And in combination with open APIs and digital data access, they can drive greater automation, data integrity and cost control, all while helping you achieve scale, deal with client demand and regulatory complexity more effectively and grow your business.

Service models are also changing to help firms adapt. Large cloud providers can now process mission-critical applications on a global basis so you can focus solely on your domain expertise. In addition, the as-a-service model is moving into new areas within the enterprise, generating additional economies of scale and a faster time to market.

Here are four examples how innovation is driving the future of financial services.

1. Decentralized finance (DeFi)

DeFi and the technology behind it – distributed ledger – sometimes have negative connotations because of their association with crypto. So why do 57% of respondents in our 2023 Global Innovation Report say DeFi is a major opportunity for growth? Because DeFi is has much wider uses than just the speculative trading of crypto currencies.

The draw of DeFi is that its technology can be used to build markets that are highly automated, faster and open 24/7. Whether tokenizing existing asset classes like stocks or bonds or being used to create liquid markets in real estate, artwork and private equity, DeFi is attracting serious attention, funding and brainpower.

Test cases are moving out of the lab and into the real world. Examples include the equivalent of a FX transaction completed over a public blockchain, the development of asset-backed tokens like stablecoins, the issuance of digital bonds over a blockchain and the exploration by central banks of issuing their own form of digital currency.

While the regulations and the technology may not be quite ready yet, these test cases show that we are rapidly heading towards a day where a large part of international payments, currency transfers and equity investment may be done in part or completely on a DeFi model.

2. ESG

Whatever way you look at it, ESG is complex. You’re trying to analyze data from all sorts of sources, from carbon and environmental data – which is easier to grasp – to “non-financial” data such as social media posts, news articles and general sentiment, which is much more difficult to quantify.

What’s even more challenging is that there’s very little standardization of data collection, calculation and metrics; not to mention company transparency and the proclivity – and sometimes even incentivization – to “greenwash.” Couple all of this with regulatory requirements that aren’t completely clear and constantly changing, and it’s no wonder that many firms are unsure if they have the right technology in place.

However, having a clear and defined ESG strategy is essential to tackle the challenges ahead. Our Global Innovation Report found that 63% of financial and fintech firms are investing in technology to improve their ESG reporting and disclosures, while 61% are investing in technology to provide more granular ESG ratings of assets and securities.

That’s really encouraging to see because ESG continues to grow fast. It’s easy to wait for regulators. But you need to invest now to capture investors’ interest in ESG opportunities – and minimize the impact of ESG on your own business.

3. Market abuse’s impact on regulatory change

After a wave of regulations around surveillance and trading, we’re now starting to see a move towards trade reconstruction. This is where you look at all of the elements and see patterns by synthesizing this information. For example, you can pull together information on whether the participants, companies or locations are on sanctions lists, specific trades and trading patterns, their communications, etc.

AI and machine learning are making this even more effective by integrating structured data and rules – for instance, is the company on a sanctions list? – with an interpretation of unstructured data such as eCommunications from multiple channels. Now you have a more complete view and richer insights while exposing previously hidden bad behavior and reducing false positives.

We’re seeing the same shift around regulatory reporting. You can bring together the underlying data, such as risk data in your systems, with reporting requirements and the nuances of how regulators want to manage and handle that information.

With that approach, you can go beyond simply delivering reports to regulators. You can organize your data more effectively, reduce the friction and labor involved in producing reports and respond to regulatory inquiries quickly and accurately.

4. Lending

In an era of heightened anxiety about macroeconomic risk, creditors need to continue lending while mitigating defaults and other adverse events. AI and machine learning are increasingly common for automating document processing, but also for making more precise decisions, from origination to servicing. Who should we lend to? What products should we lend them? Which loans on the books are at risk of default?

In the auto finance space, electric vehicles are disrupting a very traditional market, driving parallel changes in auto lending. For example, it’s no longer one driver for the life of one car. There are a lot of elements: car, battery, charging stations, etc., all of which can be funded in different ways. Plus, there are innovations such as subscriptions for fast-swap batteries that allow truck drivers to keep moving. Meanwhile, the sales model is also changing. Cars are no longer on lots; they’re chosen online and then assembled and delivered on demand. These trends mean the credit provider needs a different model with flexibility that’s more like asset finance than traditional auto lending.

There are several other lending trends worth noting.

The foundation of much of the potential collaborative innovation will be the increasing use of application programming interfaces (APIs) that enable the sharing and co-creation of solutions between financial institutions, non-financial players and third-party providers. These collaborations will enable the collaborative forces to explore alternative products, methods of service delivery and even revenue models while providing a vastly improved and seamless experience for the end customer.

Trends picking up in 2023 that will be more important in future years

ESG means not just reporting on ESG, but incorporating ESG principles into credit policy, pricing and the full lending lifecycle, from origination through servicing.

Lending-as-a-service, which is one type of embedded finance, is also starting to change the market. This is not just consumer buy now pay later (BNPL); it’s expanding to small business BNPL, revolving lines of credit, lease-to-own and more. Nor is this just for fintechs. It can also be an opportunity for financial institutions to extend their reach and participate in the new economy.

Finally, blockchain will remove a lot of manual effort from managing complex and syndicated loans, which have long lagged compared to the more digital world of consumer lending. So, even without radical disruption of the overall market, we expect to see less friction as technology makes highly manual tasks simpler and easier.

It’s easy to cross innovation off the list when economic pressure is high. But it’s clear that investing in technology is the only way to increase your agility, accuracy and ability to control costs – all of which puts you in a stronger position to compete too.