Introduction

In the finance sector, credit risk assessment and informed decision-making are critical, particularly in client underwriting, where financial institutions evaluate the creditworthiness of potential borrowers (Goodfellow, Bengio, & Courville, 2016)1. Traditional underwriting methods are labour-intensive, time-consuming, and susceptible to human bias via opaque heuristics. But with more and more data being available, deep learning technology is transforming this crucial process, making it more efficient (Chollet, 2018)2 and more systematic. This transformation is not only limited to the finance sector but is also being embraced across various industries, with deep learning playing a key role in the future of business (Ammanath & Firth-Butterfield, 2022)3.

Deep Learning and Its Significance in Finance

Deep learning, a machine learning subset, uses artificial neural networks to model and solve complex problems. In finance, deep learning algorithms can analyse vast amounts of time-series data, revealing patterns and insights that humans find hard to detect (Deng, Huang, & Tah, 2020)4. This technology has the potential to streamline the underwriting process, as demonstrated by Valerian. Furthermore, deep learning can improve productivity, increase retention, and drive revenue if companies use data to their advantage (Ammanath & Firth-Butterfield, 2022)3.

One type of neural network particularly suited for analysing time-series financial data is the Recurrent Neural Network (RNN). RNNs are designed to recognise patterns across time, making them ideal for financial data which is inherently sequential and where past events can influence future outcomes (Kumar & Rangarajan, 2020)8.

Deep Learning: Revolutionising Financial Analysis

Deep learning algorithms use a layered architecture, where input data is passed through an input layer, propagated through multiple hidden layers, and finally reaches the output layer. This allows deep learning models to automatically learn features from the data, handle large and complex datasets, and achieve top-notch performance on a wide range of problems (Goodfellow, Bengio, & Courville, 2016)1.

Deep learning offers numerous benefits in financial analysis. For example, deep learning models can handle large and complex datasets, making them a useful tool for pattern matching in large data sets. Additionally, deep learning models can handle missing data and still make accurate predictions, which is useful in real-world applications where data is often incomplete (Chollet, 2018)2.

Efficient Client Underwriting with Deep Learning: The Case of Valerian

Valerian has embraced the power of deep learning. The company has developed an innovative approach to client underwriting, using artificial intelligence and neural networks to analyse proprietary data. This approach has led to significant improvements in the speed and efficiency of their underwriting process.

For instance, by automating the analysis of time-series data, the company has been able to reduce the time required for underwriting decisions from several days to just a few hours. This not only improves the customer experience but also allows Valerian to process more applications, thereby improving the applicant’s experience and ultimately leading to a better product.

Moreover, the use of neural networks has improved the accuracy of Valerian’s risk assessments. Neural networks can identify complex patterns and relationships in data that humans find hard to detect. This has led to more accurate risk assessments, reducing the likelihood of defaults and improving the overall profitability of Valerian’s advance portfolio (Sirignano & Sadhwani, 2020)5.

Deep Learning in Finance: Limitations and Data Security

While the benefits of deep learning in financial analysis are clear, there are also potential concerns and limitations. For instance, training deep learning models requires significant computational resources, which can be costly and time-consuming. Additionally, deep learning models can be complex and difficult to interpret (Pozen & Ruane, 2019)6.

Data security is another significant concern when applying deep learning in finance. With the increasing prevalence of cyber threats, ensuring the security and privacy of sensitive financial data is paramount. Valerian takes this issue very seriously and has implemented robust data security measures to protect its clients’ information. The company uses advanced encryption technologies and follows strict data handling protocols to ensure that all data used in its deep learning models is secure and confidential.

However, it’s important to note that deep learning is not a silver bullet. It requires sound governance structures to ensure positive results and to address potential issues such as bias. As we move forward, it will be crucial to develop these structures to ensure that deep learning can be used effectively and ethically in finance (Wellers, Elliott, & Noga, 2017)9.

Intelligence-Based Funding and Deep Learning

Intelligence-based funding (IBF), as implemented by Valerian, represents a transformative approach to investment that leverages the capabilities of deep learning. Unlike traditional revenue-based funding, IBF continuously analyses a business’s growth strategies to measure economic value creation, making it a market-resilient financial model.

At the core of IBF is the continuous learning process enabled by deep learning algorithms. These algorithms not only test to ensure that the business is growing and that the founders are making the right decisions but also refine and enhance their insights over time. This continuous learning benefits from data network effects; the more it is engaged by a founder, the more precise its insights become, leading to better funding terms.

What sets IBF apart is its potential to serve whole sections of the market currently underserved by traditional lenders, who often exhibit risk aversion. By employing deep learning, intelligence-based funding can offer a more nuanced and adaptive risk assessment, opening doors for businesses that might otherwise struggle to secure funding. This innovative approach not only democratises access to capital but also fosters a more dynamic and responsive financial landscape.

The integration of deep learning into IBF allows for real-time adjustments based on the latest data, ensuring that the funding model remains agile and aligned with the ever-changing market conditions. In a world where financial stability and growth are paramount, intelligence-based funding offers a visionary path forward, harnessing the power of artificial intelligence to create a more inclusive and resilient financial ecosystem.

Conclusion

The advent of deep learning technology is revolutionising client underwriting in the financial industry. By leveraging deep learning models to analyse time-series data, providers of capital like Valerian can make more accurate risk assessments, improve efficiency, and enhance customer satisfaction. The introduction of intelligence-based funding further illustrates the innovative applications of deep learning in finance, promising a more inclusive and adaptive financial landscape. As the field of deep learning continues to evolve, we can expect to see even more innovative applications of this technology in the financial industry (Heaton, Polson, & Witte, 2020)7.

Stay tuned for the next article in this series, where we will delve deeper into dealing with seasonality in the treatment of time series.

Footnotes:

1. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Retrieved from http://www.deeplearningbook.org/

2. Chollet, F. (2018). Deep Learning with Python. Manning Publications. Retrieved from https://www.manning.com/books/deep-learning-with-python

3. Ammanath, B., & Firth-Butterfield, K. (2022). Deep learning will play a key role in the future of business. World Economic Forum. Retrieved from https://www.weforum.org/agenda/2022/01/deep-learning-business-productivity-revenue/

4. Deng, L., Huang, G., & Tah, M. (2020). Deep Learning in Finance: A Review. Frontiers in Artificial Intelligence. Retrieved from https://www.frontiersin.org/articles/10.3389/frai.2020.00060/full

5. Sirignano, J., & Sadhwani, A. (2020). Deep Learning for Mortgage Risk. SSRN. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2799443

6. Pozen, R., & Ruane, J. (2019). What Machine Learning Will Mean for Asset Managers. Harvard Business Review. Retrieved from https://hbr.org/2019/12/what-machine-learning-will-mean-for-asset-managers

7. Heaton, J., Polson, N., & Witte, J. (2020). Deep Learning in Finance. ArXiv. Retrieved from https://arxiv.org/abs/2002.05734

8. Kumar, A., & Rangarajan, K. (2020). Financial Forecasting With α-RNNs: A Time Series Modeling Approach. Frontiers in Applied Mathematics and Statistics. Retrieved from https://www.frontiersin.org/articles/10.3389/fams.2020.551138/full

9. Wellers, D., Elliott, T., & Noga, M. (2017). 8 Ways Machine Learning Is Improving Companies’ Work Processes. Harvard Business Review. Retrieved from https://hbr.org/2017/05/8-ways-machine-learning-is-improving-companies-work-processes