Wealth Management
Mortgage digitalization: How mortgage lenders are automating the lending process
Published
5 months agoon
By
admin
By Fernando Zandona, Chief Product and Technology Officer at Mambu
The mortgage market has a long history, but its future is digital. As tech capabilities grow and consumer expectations evolve, mortgage providers are increasingly turning to digital solutions to attract and retain customers and streamline the lending process. According to research from the 2022 Celent Origination Study, over half of banks and 75% of building societies expect to make significant changes to their mortgage origination systems within 24 months. So, how is the mortgage industry transforming and what must lenders do to future-proof their business?
The acceleration of digitalisation in mortgage lending
There are several factors that have accelerated the digitalisation of mortgage lending. One is changes to consumer behaviour: customers have come to expect smooth digital experiences across all areas of their life (accelerated by the pandemic). As such, they seek similar ease, speed and efficiency when it comes to home buying.
Then there’s the arrival of fintechs. Newer fintechs are beginning to enter the mortgage sector – often through acquisitions, such as Starling Bank’s acquisition of Fleet Mortgage or Zoopla acquiring YourKeys. They are also bringing with them innovative digital solutions, which raise the bar for the whole industry. At the same time, regulatory changes are helping accelerate and facilitate digitalisation, such as the Bank of England’s decision to withdraw its affordability test recommendation and cut some of the red tape around mortgage lending, and HM Land Registry’s acceptance of electronic signatures. The combination of these forces have played a significant role in accelerating the lending process and making it more efficient.

Fernando Zandona
Today’s financial institutions are offering a wide range of digital options, through online and mobile platforms, to their mortgage customers. Services include easier ways for customers to access and manage their mortgages, schedule a session with a mortgage advisor, find personalised recommendations, and access improved security measures to protect sensitive customer information.
That’s not to mention the embrace of open banking has enabled seamless integration of customer data into the lending process. This innovation is helping reduce the number of steps needed to collect data and resulting in faster processing times, less rekeying of information and lower origination costs. Offering faster, cheaper loan decisions is a crucial advantage in an increasingly-crowded mortgage market and automated processes reduce teams’ manual work and eliminate costly human errors.
Digitalising in the right way
The success of these new products and processes relies on the way mortgage lenders introduce and configure them. Agility is key – lenders need to prioritise configurability and scalability when building new products and choosing technology partners, as they must be able to quickly launch new features or make adjustments, in line with evolving customer expectations, emerging trends and changing industry regulations. The use of software-as-a-service (SaaS) platforms and application programming interface (API) integrations helps with this, allowing for faster feature launches and less internal friction.
APIs are just part of future-proofing the mortgage market. According to Forbes, 55% of senior executives in the US mortgage industry think that AI will make their firm, and the industry overall, more competitive. AI and machine learning can assist lenders in analysing data more quickly, leading to more efficient decision-making and forecasting, although as with all AI applications, providers must be vigilant about encoded bias that can radically increase discrimination.
The mortgage landscape is transforming through digitalisation, and this is bound to continue. Lenders who want to keep up the pace with this change – and reap the benefits of faster, smoother processes as well as keep satisfied, loyal customers – will be future-proofing their processes through lending automation and putting customer ease at the centre of their offering.
Business
Exploring the Transformative Potential and Ethical Challenges of AI in Wealth Management
Published
2 days agoon
September 26, 2023By
adminNuno Godinho, Group CEO of Industrial Thought Group
In recent years, the advent of AI has sparked both excitement and scrutiny within the Wealth Management industry. The technology’s capabilities, including but certainly not limited to generative AI algorithms like ChatGPT, offer a new dimension to data analysis, market prediction, and portfolio management. However, while it presents a promising avenue for enhancing decision-making and elevating client interaction, AI also carries inherent challenges that demand careful consideration.
Benefits of AI in Wealth Management:
In a world where CX is key, AI enables wealth managers to provide personalised advice, improved portfolio performance, real-time insights, and convenient access to information and support. Previously it has been impossible for advisors to deliver hyper-personalisation at scale; now, AI-driven customisation lets them tailor investment strategies and recommendations to their clients’ unique financial goals, risk tolerance, and investment horizon.
AI algorithms can also analyse vast amounts of data to identify trends and opportunities, resulting in potentially higher returns on investments. And, more widespread use of automation will gradually reduce the cost of wealth management services, meaning higher-quality investment advice at a lower price. This is critical as firms fight to stay relevant for modern investors disillusioned by traditional advisory firms and private banks.
Relationship-wise, there are many other advantages. AI-driven data analytics make it easier to gain a deeper understanding of an investor’s needs, preferences, and behaviours, all of which help to build long-term relationships. Through predictive analytics, firms can differentiate their service and proactively identify new investment opportunities, such as emerging market trends or underperforming assets. At the same time, chatbots and virtual assistants facilitate constant communication to answer queries and increase engagement. By strategically integrating AI technology into their operations, firms have the power to optimise top and bottom lines, strengthen client connections and position themselves for long-term growth.
Navigating the Ethical and Practical Challenges:
While AI holds remarkable potential, major obstacles must be overcome. With AI’s reliance on large amounts of data, ensuring client data confidentiality, managing consent, and complying with global data protection regulations like GDPR are significant challenges. Another issue is algorithmic bias – as AI learns from data, it may inadvertently perpetuate inequalities or biases present in the training datasets used. Vigilance is necessary to ensure that AI systems don’t amplify these issues. A key concern is the absence of standard governance, leading to a lack of accountability and transparency. Black-box algorithms can make decisions without providing clear explanations for their reasoning, making it difficult for clients and regulators to understand and trust AI-driven outcomes. Overall, the responsibility for AI-generated recommendations remains complex, requiring collaborative efforts to establish robust regulatory frameworks.
Striving for Data Integrity and Reliability:
The efficacy of AI-driven solutions hinges on the quality of training dataset they are supplied with and rely upon. Therefore, ensuring accurate, unbiased, and comprehensive datasets is paramount to generating trustworthy insights. The absence of standardised data sharing can lead to skewed results, ultimately impacting the quality of AI-generated advice. Transparency in data usage, validation, and generation reasoning will be pivotal to cultivating client trust and minimising systemic risks, which ties back to the absence of standard governance, as the output from AI-generated advice will only be as good as the data sets provided. We need to understand the “lineage” of all data used and generated by the algorithms. Until the industry can come to some accord on how we plan to use all of our respective data, it will be prone to various biases and fragmented advice, which will lead to liability and reliability issues down the line. It’s worthwhile wondering whether we can see the industry opening up in an age of data equals value.
The Role of Collaborative Partnerships:
Amidst these challenges, collaborative partnerships emerge as a potent avenue. Established wealth management firms can harness the expertise of FinTech AI companies to augment their capabilities while mitigating the risks associated with AI adoption. A symbiotic relationship, where innovative AI solutions are developed by trusted partners, helps safeguard against potential pitfalls and aligns with the pursuit of ethical, data-driven decision-making.
Looking Ahead: Striking a Balance for Sustainable Progress:
As we journey into the AI-powered future of wealth management, it’s evident that a balanced approach is essential. The integration of AI has the potential to expedite the transition to wealth management 4.0, revolutionising personalised client experiences and advisory services. However, this progress must be underpinned by clear ethical guidelines, data integrity, and collaborative partnerships. Striking this equilibrium promises not only a more informed, efficient, and personalised industry but also one that upholds the principles of transparency, accountability, and client trust.
In conclusion, AI’s impact on the wealth and asset management landscape is profound, offering unparalleled insights and opportunities. While navigating challenges will be crucial, a collective effort to harness AI’s power while ensuring its responsible application will pave the way for a resilient, future-forward industry.

~ The benefits of AI when collecting and analysing financial data ~
Global fintech company Finder reported that around two in five people in the UK (42 per cent) currently invest, whether it’s in stocks and shares, funds or properties. Younger people are particularly interested in investing, with 60 per cent of members of Gen Z saying they have invested before. Data plays a pivotal role in managing these investments, according to Finder’s report. So how can wealth management companies streamline data collection, analysis and management? Here Alex Luketa, partner at artificial intelligence (AI) data management specialist Xerini, explores how wealth management companies can benefit from AI.
Wealth management firms collect various types of data to effectively manage their client’s portfolios. Data helps these companies understand their clients’ particular situations, goals, any risks and investment preferences. Finance managers can also analyse market trends, portfolio risks and other factors to make investment decisions and protect their clients.
Effectively managing this data can be difficult, particularly when it’s stuck in different systems and formats, meaning finance managers must use spreadsheets to consolidate everything they need. Building a data warehouse that copies all the data from systems across the business into one platform can resolve this issue, but it can also be a time-consuming and complex process. Putting the data in one place takes time and the copying process is only updated periodically, meaning that users cannot always access the most up-to-date information.
Streamlining data management
Proper data management is key to building trust with clients, keeping their data confidential, providing the best advice and maintaining integrity of the process. As a result, to remain competitive, wealth management companies should consider how they can streamline data management.
When planning to improve operations, wealth management companies should look at where they can make the most valuable gains. For example, the more time finance managers are spending rifling through different systems to find what they need and filling in spreadsheets, the less they can focus on sharing valuable advice with clients. So, how can they more effectively carry out these processes?
Enter artificial intelligence
Some businesses use data warehousing as a data management strategy, but this requires an expert to copy all the necessary information. While warehousing results in more accurate data, creating it is a time consuming process and periodic batch processing makes it difficult to see the most up-to-date information. Alternatively, more businesses are exploring how AI tools like ChatGPT can deliver business value in a range of applications and industries, including wealth management.
A cloud-based, AI management system centralises data across different systems and provides businesses with the ability to review and report on real-time metrics quickly and efficiently. Unlike warehouses, a cloud-based system leaves data where it is, hosting the information on one interface rather than splitting it between different systems, rapidly reducing the time required for reporting and data management.
Wealth management firms will deal with convoluted and diversified portfolios stored across various systems. Cloud-based data management systems, such as Xefr, are built to have one unified interface that can offer a single, comprehensive view of each portfolio, ensuring more informed decision-making. Additionally, to help better personalise investment strategies, systems like Xefr can convert complex datasets into valuable insights. With interactive querying, the firm can quickly access factors such as market trends, client risk appetite and portfolio performance to create customised advice.
Talk to your data
Interpreting complex data sets is not simple, meaning these platforms may not make it easier for everyone in the business to find and analyse the data they need. However, by integrating large language models (LLMs), businesses can create interactive interfaces that any user can confidently navigate. For example, by training the system on relevant prompts using natural language, users can ask questions of their data. Users can describe what they want the report to look like and the data it needs, and build a dashboard.
At a glance, users can interrogate existing client data alongside information such as market trends and risk to provide more effective advice without the need to rifle through manually-made reports. This means team members can spend the time saved on reporting on more valuable tasks.
Overcoming AI barriers
Businesses that are willing to rapidly adopt emerging technologies like AI could see significant benefits in automating laborious tasks, such as reducing costs and improving data integrity. While many businesses may see the potential gains, it is understandable that some are apprehensive.
When new technologies are introduced that automate tasks, some team members may be cautious that they will be replaced. In reality, AI still needs human input to interpret information and provide valuable prompts. Also, looking back at previous innovations, the computer nor the internet replaced us, they enhanced people’s work — AI is predicted to do the same.
Wealth management businesses handle confidential client information on finances, personal details and more. Using open platforms like ChatGPT raises privacy concerns, with a lot of data and queries being visible to software developers. Building a private platform with natural language processing capabilities enables wealth management businesses to ensure privacy, and developers can build barriers around data sets to ensure only authorised users can access private data.
As more people explore the benefits of investing, wealth management firms are looking at how they can improve efficiency, reduce costs and remain competitive. Developing a cloud-based data management system and leveraging AI allows businesses to streamline reporting, which frees up valuable time and provides more visibility for making decisions based on data. It also enables users to converse with their data, better understanding how they can use all the information at their disposal to provide a competitive edge to client portfolios.
Magazine
Trending


In-platform solutions are only a short-term enhancement, but bespoke AI is the future
By Damien Bennett, Global Director, Principal Consultant, Incubeta If you haven’t heard anyone talking about artificial intelligence (AI) yet,...
Exploring the Transformative Potential and Ethical Challenges of AI in Wealth Management
Nuno Godinho, Group CEO of Industrial Thought Group In recent years, the advent of AI has sparked both excitement...


Are SaaS platforms challenging banks for a piece of the payments pie?
Attributed to: Ralph Dangelmaier, Global CEO of BlueSnap The finance industry is at a tipping point with software firms...


Emerging technology will power long-term sustainability within the UK banking industry
By Peter-Jan Van De Venn, VP Global Digital Banking at Hexaware Mobiquity. Sustainability has been a big focus for...


Is your business suffering with Fintech FOMO?
Tom Kiddle, Chief Commercial Officer at Equals Money It’s a challenging time for businesses of all sizes, but the past three...


The Future of Banking: Streamlined Cash Management for ATMs
Gaetano Ziri, Innovation Manager, Auriga “Maintaining free access to cash for the community demands robust strategies to mitigate the...


Can AI revolutionise wealth management?
~ The benefits of AI when collecting and analysing financial data ~ Global fintech company Finder reported that around...


Where is the value in generative AI for financial services?
Michael Conway, Executive Partner, Data, AI and Technology Transformation Service Line Leader at IBM Consulting The New York Times...


Connecting the security dots with cyber fusion
Anuj Goel, Co-founder and CEO at Cyware Against the backdrop of Russian-based hacktivists declaring war on Europe’s financial systems, the...


Exploring the symbiotic advantages of SoftPoS for merchants and consumers
By: Brad Hyett, CEO at phos by Ingenico Amid the dynamic shifts that have come to define today’s fintech...


Investing In Bitcoin: What You Need To Understand Before You Buy
Bitcoin—the digital currency that launched a financial revolution—is more than a trending investment. This decentralized currency, free from traditional banking...
How the LEI Can Help Financial Institutions ‘Address’ a Growing Challenge in ISO 20022
The vast complexity and inconsistency of address formats globally presents significant challenges for financial institutions. In this blog, GLEIF’s Head...


Building towards an inclusive financial future
By Catharina Eklof, CCO of IDEX Biometrics From the visually impaired to displaced migrants, the unbanked, and people living...


Euro deep tech M&A deal value expected to reach $20bn+ in the next 15 months
Written by Oliver Warren, Associate at DAI Magister Investment in European deep tech has mirrored the broader decline in...


Why ESG Investing Is Becoming More Important
Author: Urtė Karklienė, Sustainability Manager at Oxylabs Environmental, social, and governance (ESG) term was first mentioned in a 2004...


Preparing banks for digital transformation
By Joman Kwong, Strategic Solutions Manager, Financial Services at Laserfiche Today, digital transformation is imperative for every industry. After...


The critical tech to deliver personalised digital financial experiences
Jay Sanderson, Senior Product Marketing Manager, Digital Experience at Progress Providing customers with outstanding digital experiences is now a must...


Bank-fintech partnerships can shape the future of cross-border payments
Steve Naudé, Head of Wise Platform People and businesses are more interconnected than ever. In today’s global economy, international...


DORA Compliance in Financial Organisations: What You Need to Know
Nick Hogg, Director of Security Training, Fortra The regulatory landscape is tightening for European banking, financial, and insurance institutions....


How sound investment research can revive the City of London
Author: Neil Shah, Director at Edison Group A few months ago, leading portfolio manager Nick Train described the modern...

In-platform solutions are only a short-term enhancement, but bespoke AI is the future
Exploring the Transformative Potential and Ethical Challenges of AI in Wealth Management

Are SaaS platforms challenging banks for a piece of the payments pie?

Emerging technology will power long-term sustainability within the UK banking industry

Is your business suffering with Fintech FOMO?

The Future of Banking: Streamlined Cash Management for ATMs

PCI DSS v.4.0 Latest Updates That You Need to Know

RBI’s MASTER DIRECTION ON DIGITAL PAYMENTS SECURITY CONTROLS

EMV® 3-D SECURE: ENABLING STRONG CUSTOMER AUTHENTICATION

HOW TO SIMPLIFY IDENTIFICATION IN THE GLOBAL DIGITAL ECONOMY WITH THE LEI

EXEGER – CHANGING THE PERCEPTION OF POWER

FUTURE FX PROMO
Trending
-
News4 days ago
How the LEI Can Help Financial Institutions ‘Address’ a Growing Challenge in ISO 20022
-
Finance3 days ago
Investing In Bitcoin: What You Need To Understand Before You Buy
-
Banking2 days ago
Emerging technology will power long-term sustainability within the UK banking industry
-
Business2 days ago
Exploring the Transformative Potential and Ethical Challenges of AI in Wealth Management