Embedded finance allows to pay for a purchase online without entering bank details or instantly take out a consumer loan on digital platforms outside banks, among many other options. This Bank-as-a-Service model, which allows the integration of financial services via APIs, moved $22.5 billion in 2020, a figure that will increase tenfold in the next four years.
To meet the rising demand for embedded finance, financial institutions are increasingly offering banking as a service (BaaS)—bundled offerings, often white-labeled or cobranded services, that nonbanks can use to serve their customers. Making it work will require new technologies and capabilities, because BaaS is usually distributed to clients via APIs and requires strong risk and compliance management of the embedded finance partner. (Fintechs offering to intermediate BaaS relationships also have emerged; examples include Treasury Prime, Synctera, Unit, and Bond.) Banks will also need new business models, such as pay-for-use monetization, B2B2C and B2B2B distribution capabilities, and a careful consideration of branding.
We see new trends in the embedded-finance and banking-as-a-service arena. Understanding and monitoring these trends can help banks, and those who hope to work with on embedded finance, identify opportunities and guard against threats.
Intelligent Process Automation is the combination of different technologies to automate more complete, end-to-end business processes. It is the evolution of basic, rules-based task automation into the management and automation of entire business processes made up of numerous tasks.
These technologies can be used for:
1. Automating front to back-office processes;
This high degree of manual processing is costly and slow, and it can lead to inconsistent results and a high error rate. IT offers solutions that can rescue these back-office procedures from needless expense and errors.
Significant opportunity exists to increase the levels of automation in back offices. By reworking their IT architecture, banks can have much smaller operational units run value-adding tasks, including complex processes, such as deal origination, and activities that require human intervention, such as financial reviews.
2. Automating investor data migration and data processing;
There is no need to have any system replication between firms, whether it is portfolio management, CRM or AML, accounting, HR, or any other system for an extended period of time post-acquisition. IPA technology can migrate any data to a chosen system in days or weeks rather than months or years. Using digital workers to do this means that no mistakes are made, best practice is always followed, and the work is carried out 24/7. A single digital worker is the equivalent of eight full-time employees. Utilizing them means that centralizing data, a process that typically takes months, can be reduced to days while simultaneously reducing risk.
3. Digitalizing paper-based data
Despite the fact that paper is still widely used in wealth management and financial advice, OCR technology has been around for decades. If the acquired firm has paper records, relevant data can be extracted, classified, verified, and input directly into any system of choice.
4. Digital communication
This year, we can expect to see the deployment of a variety of new and innovative services and solutions that will provide a clear competitive advantage to the most forward-thinking and client-focused firms. Presently, digital communications via secure chat channels are likely to become more common and in demand by investors as part of standard service provision. The key to success will be to provide one-of-a-kind digital services with a premium feel and a clear market differentiator. AI solutions, being highly complex technology, will require the same degree of controls and inspection (such as service organization controls [SOC 2] reports) as other essential business operations.
At its core, Intelligent Process Automation is the convergence of RPA and different Artificial Intelligence (AI) technologies to automate larger decision-based business processes that traditionally required an employee to intervene and execute.
Despite all of these realized and potential benefits, many AWM organizations seeking to use AI are getting stuck — often in the same locations. One of the most significant barriers to AI in AWM firms is natural skepticism. Asset managers are frequently focused on risk reduction — and many AWM executives are concerned that if AI solutions aren’t completely trustworthy, they may introduce new hazards. Top AI-related concerns raised by asset managers in PwC’s survey include the possibility of cyber and privacy threats — 71 percent of respondents said that responsible AI tools to assist with privacy and cybersecurity issues would be a primary concern in 2021 — and concerns that they may not understand exactly how an AI tool is making conceivably important decisions.
Making a business case for AI is hindered further by a culture that is widespread in many firms: one that is cautious of developing technology and may lack the attitude to fully employ them. After all, many businesses have a stable book of business. They may not see the need to innovate as vital. However, because AI is now giving so many benefits to so many asset managers, this prudence may generate a danger – falling behind the competition.
KIELTYKA GLADKOWSKI KG LEGAL actively advises clients in a wide spectrum of legal aspects of implementing technologies in asset and wealth management.
WealthTech2022 – Part of The WealthTech Views Report Series