By Jannet Smith 
Introduction
In the current world which is characterised by the high use of technology especially in the field of Finance the industry has been greatly transformed. Some of the conventional ways of dealing with financial affairs need to be revised to address the various and challenging requirements of today’s economies. However the integration of human input with the best technological tools has proven to be a strong strategy for financial innovation in addressing these challenges and this is what has been referred to as a hybrid model.
This model builds up the strengths of human decision making and technological efficiency and creates a balance when it comes to the process of decision making managing and preventing risks and serving customers. This is not a trend but an essential that in the financial market with constantly emerging novelties has the primary purpose of providing a safety cushion.
Analysing the Elements of Hybrid Model
The hybrid model in Finance is the combination of human focalistic with artificial intelligence (AI) machine learning (ML) big data and blockchain. While the common conventional financial models involve exclusive human interventions or complete mechanisation this model aims at applying integrated approaches in equal manners as aiming at the absolute perfection of both extreme approaches.
In Finance hybrid models can also be of different types some of which are hybrid advisory and hybrid operating where there is a mix of robo advisors and human financial advisors and automated trading with manual control respectively. This is based on the assumption that both human and machine modes are incomparable in some tasks but are also equally prone to weaknesses that complement each other.
Innovative development of Hybrid models
Hybrid models in Finance can be defined as a relatively new phenomenon in the financial industry as long as it leans on the fruits of technological advancement. In the past most of the activities were done manually and human beings applied their knowledge skills and intuition to solve problems. This approach as useful as it was particularly associated with two major issues overwhelming volumes of information and the relative sophistication of financial markets.
The virtual and new economy that evolved in the last decades of the Twentieth and early Twenty First century made a major difference in the ways of conducting financial activities. Technological advances over the years such as the use of automated trading systems algorithmic trading and digital wallet banking have come into play changing the methods through which people conduct banking. However these technological models in the early stages could have a different degree of flexibility and ability that judgement could allow.
Use Of Technology
Lime this paper has established that technology is instrumental in the functioning and productivity of hybrid models in the field of Finance. Several key technological advancements have made it possible to integrate human expertise with automated systems effectively.
Artificial Intelligence (AI) and Machine Learning (ML)
The financial industry has been transformed by AI and ML by offering better forecasts customized services and efficient risk assessment solutions. Such technologies could easily process big data and come up with patterns and trends that a human being might conceal. The figure for the integration of AI and ML into Finance is hybrid where the AI works in cooperation with human input and the insights given by the AI help improve the effectiveness of financial operations.
Big Data and Analytics
Real Time data processing and analysis is now a foundation that defines the current state of affairs in the field of Finance. The application of big data analytics in ASRB helps big institutions in the financial field analyse and understand trends that depend on the market in operation customer behaviour and risks. In the hybrid approach these findings are applied to human decision making to provide a more precise and swift reaction to the shift in the market situation.
Advantages of the Hybrid Model
In general the hybrid model is beneficial for the financial industry since it integrates decision making risks costs and customers. Thus the benefits described above prove that the hybrid model is a worthwhile asset when it comes to a fast evolving financial environment.
Enhanced Decision Making
The hybrid model greatly enhances decision making by combining the advantage of the gut feeling and the numerical calculations flexibility. Human professional understands the market much better the rules and regulations of the market and the ethical factors to be considered. Technology on the other hand offers vast data support and updated analysis.
For instance in investment management the hybrid models enable portfolio managers to leverage AI tools and adapt market analysis for the purpose of making appropriate investments in assets. They differ from their human counterparts to the extent that electronic tools can often analyse large quantities of material and work out where the opportunities and threats lie without necessarily having to dredge them up from the depths of one’s consciousness.
On the other hand human managers can discuss the last investments and the final results using knowledge and experience to make the investments in the realisation of such factors as market changes and long term goals.
Use of Hybrid Models in Finance
In the following section this paper shall explore the fact that as much as the hybrid model is full of benefits it has been with some impediments. There are several challenges that financial institutions have to face and overcome to ensure the integration of human capital with expert tech equipment.
Integration Challenges
An overarching problem with the hybrid work model is the blending of the company’s existing IT platforms with current technologies. Most of the financial institutions in the present world have yet to modernise their infrastructure which is not compatible with the latest technologies such as AI blockchain and cloud computation. Implementing these systems is not a walk in the park and can be very expensive as it needs a lot of investment in terms of infrastructural development and IT.
There is also a problem of compatibility with respect to trying to integrate one technological tool or platform into another in the context of scope to a hybrid model. It is therefore equally important that these systems are able to interconnect and operate coherently in order to achieve the effectiveness of the hybrid model.
Data Privacy and Security
This is so because the two main issues that have a significant impact on the use of hybrid models in Finance data privacy and security. Applied technologies including AI big data and blockchain in financial services demand the processing and storage of substantial volumes of consumers and business peoples valuable financial data. This generates certain vulnerabilities concerning data leakage hacking and unauthorised access to sensitive data.
Aside from these risks together with many other regulatory obligations that have to be met financial institutions are bound by data protection laws like the GDPR in Europe and the CCPA in California.
FedEx Corporation
FDX is an American multinational delivery services provider company with its operating company. FedEx Express being the world’s largest express package delivery company and also the freight forwarding division. FedEx Integrated Operations being the world’s second largest cargo airline and freight company wherein the valuable feedback was obtained from the firm’s FedEx Express operating (PA) company in the United States.
These regulations specify the provisions with regard to the processing of customer data by financial institutions including the reception of consent data accuracy and their customers right to access and erase the data.
To overcome these challenges it is a prerequisite that financial institutions ensure some specific safeguards related to data privacy and security such as encryption user control and regular security checks. There must also be sound policies and procedures when it comes to handling data and it should also be conducted with its employees on how data should be protected.
Other real case studies have revealed that implemented HM can be exposed to numerous security risks if data privacy and security are not properly tackled. For instance in 2020 a large bank was attacked due to the security menace that was lurking in the AI construct that was to help in risk management. Recent hacking involved the exposure of customers personal information as well as a lot of losses on both financial and reputational levels.
Human Machine Collaboration
Another issue related to the use of supervision within the hybrid models is the tendency to balance the use of automated systems while attempting to retain overriding human control. Even though relying upon machinery tools many operations and actions can be performed steadily and efficiently. Human reaction experience and decision making abilities are still crucial in decision making critical meetings and proper responses to ethical issues.
However working together with machines has its difficulties and overcoming this barrier is the focus of this paper. This is because financial professionals may be reluctant to adopt new technologies since using new technology means that your job will be automated or down played. On the other hand relying strictly on big data and analytics without sufficient human intervention may result in mistakes biases and ethical problems.
The solutions to these challenges lie in creating organisational cultural acceptance of cooperation and innovation in which technology integrated into their operations is viewed as an enabler of human resources. This may entail making available fifty training and development opportunities for the employees creating an environment where the employees feel free to engage with one another and or advancing employees understanding of the ethical applications of technology.
Regulatory Compliance
Hybrid models complex nature poses a considerable problem in regulation specifically for financial institutions. Specific changes can differ depending on the legal system of a certain country and financial laws are constantly being developed since they aim to protect the sphere from new risks and problems created by developing technologies.
Hybrid models are subject to a plethora of rules among which are rules that concern data privacy cybersecurity AML and consumer protection. Compliance with such regulations is a challenging task that can take a lot of time and can be expensive in terms of resources and workforce.
Hybrid models in financial institutions require regulation compliance strategies that capture various rules that will apply to such institutions. This may include engaging in extensive collaboration with the regulatory authorities conducting compliance checkups often and putting in place compliance and reporting effective mechanisms.
Applications
The hybrid models have been successfully applied in different areas of the financial market which proves the efficiency of such approaches. The following are the uses of hybrid models in retail banking investment management and insurance industries and fintech.
Retail Banking
Robo Advice as a combo with human beings is quickly evolving as a trend in retail banking since many clients are willing to have their wealth managed by a machine yet they initially consult experts. Automated financial advisors or robo advisors like Betterment and Wealthfront which help to select stocks and mutual funds automatically for the customer depending on their needs and risk capacity or timeline. These suggestions are then looked at by the human consultants to give further assistance if necessary.
Hybrid models are also becoming irresistible in customer service with digital transformation efforts. Currently many banks have incorporated the use of artificial intelligence in conversational interaction interfaces particularly in text type in the form of chatbots and virtual personnel in attending to basic customer inquiries including account balance and transaction statements.
These tools can make quick and certain responses for customers while human agents will handle issues that require more attention. Similarly customers have the opportunity to connect with a human operator if they need any further help. Several global and national banks like Bank of America and HSBC etc have proved that the both centralised and decentralised services improves the overall satisfaction level and retention ratio of their customers.
Investment Management
Portfolio management also uses hybrid models where computational techniques are applied together with human intervention as rate judgement by portfolio managers. AI and machine learning are utilised to trade algorithms and make decisions for trading in the portfolio. These systems are capable of handling vast data and making choices in a split second which is otherwise very time consuming for traders.
Nonetheless human portfolio managers are still involved in the sense that they supervise trading actions and make the last word verdicts on investment decisions. They can use their experience and feelings to estimate the market situation make the needed changes and respond to the risks that can occur.
Real Life applications from hedge funds and funds management firms like Bridgewater Associates and BlackRock will show how such hybrid frameworks enhance investment returns and minimise risks.
Insurance
Employment of hybrid underwriting models in the insurance industry is increasing due to their ability to improve underwriting decisions. Customer characteristics such as age gender and perhaps health records and sentiments posted on social media may be used to measure risk propensity and set premiums among other uses. It can offer faster and more accurate underwriting decisions compared with conventional workflow methods.
Subsequently human underwriters analyse the AI recommendations and provide their subjective endorsement to make the decisions more fairly ethically and more pertinent to the regulation.
It is also applied to claims processing where AI can facilitate the automation of tasks like confirmation from documents and fraudulent activities. Still human claims adjusters are important in the working process as they are involved in many complicated claims solving disputes and giving individual attention to clients.
FinTech and Startups
In terms of hybrid models FinTech startups are currently leading the way towards model innovations by replacing traditional values of financial services with technology ones. Currently hybrid models can be employed in any fintech product from P2P lending to payment systems and wealth management.
For instance LendingClub and PayPal incorporate artificial intelligence to evaluate credit risk undertake payments and give economic advice. Human skills back up these tools in areas that may include customer relations legal requirements and uncertainties.
Companies using hybrid models have also risen among new entrants especially targeting consumers in emerging markets by solving financially excluded individuals problems by offering affordable and easily accessible financial services.
Other examples like MPesa in Kenya or Alipay in China are vivid examples of how this hybrid business model fosters innovations in financial services and paves the way to new opportunities.
Hybrid models if they are with sustainable future prospects will be able to support many institutions in the finance field.
Hybrid models in Finance
Some of the issues that relate to the future of hybrid models are opportunities while others are considered challenges facing financial institutions.
Trends Shaping the Future
Several trends are likely to shape the future of hybrid models in Finance including Several trends are likely to shape the future of hybrid models in Finance including
Increasing Reliance on AI and ML
As the AI and ML technologies are being developed further the utilisation of such in the hybrid models is anticipated to increase. More specifically these technologies will be valuable for analysing source data drawing forecasts and automating procedures in financial institutions. However human intervention will still be needed to monitor these systems and ensure that they are used properly.
Growing Importance of Data Analytics
More specifically analytics will remain a key component in supporting a hybrid model because of its ability to help financial institutions understand customers behaviours the market and risks.
Challenges Ahead
While the hybrid model offers significant opportunities several challenges must be addressed to ensure its success.
Adapting to Evolving Regulations
The environment is dynamic and therefore institutions have to continuously deal with new rules and make sure that their hybrid models conform to the rules. This may entail interacting with regulators on a frequent basis conducting compliance checks and audits and monitoring and reporting mechanisms.
Addressing Ethical and Societal Concerns
In Hybrid models the incorporation of AI and other pricey technologies introduces other issues to the sustainability conversation including Algorithmic bias job automation and data privacy. This means that financial institutions must establish ethical standards when deploying these techniques of hybrid models to deal with these issues & set proper policies.
Conclusion
The hybrid model can be considered a revolutionary scenario of the financial industry where an organisation’s human talent and advanced technology can work in tandem to contribute to better decision making processes risk management and cost efficiency as well as an improved approach to customer relations.
Thus the Hybrid model will become even more important in the future to assist financial institutions in coping with upcoming difficulties and to take advantage of rising opportunities in the constantly changing financial environment.